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2026

A Parasitic-Tolerated Design of Compact Inverse Class-F GaN HEMT Power Amplifier With Integrated Matching Network

Nguyen, Vuong, Do & Nguyen
IEEE Access

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A novel load structure is proposed for designing an inverse class-F (class F−1) power amplifier (PA) with compact size and high power added efficiency (PAE). The proposed load network is implemented entirely with six transmission lines (TLs) and integrates all of main functions: class F−1 harmonic control, biasing, and fundamental output matching in one which typically operate in separate networks in the conventional topologies. The TL parameters of constituent elements are extracted based on derived analytical equations aided by load-pull measurements which tolerate the parasitic effects of a packaged transistor, and then neither extra parasitic compensator nor time-inefficient tuning mechanism is required while both optimal harmonic and fundamental conditions are met. As a result, compact and low insertion loss class F−1 PAis realized.With this design methodology, the implemented PAcan exhibits a state-of-the-art performance at 920 MHz with a measured peak PAE of 85.6%, corresponding to gain of 14.7 dB, and output power of 39.7 dBm, and a miniature size of 0.21λ×0.20λ.

@article{11450062, author = {Dang-An Nguyen and Dat Vuong and Danh Cuong Do and Van-Dinh Nguyen}, journal = {IEEE Access}, title = {A Parasitic-Tolerated Design of Compact Inverse Class-F GaN HEMT Power Amplifier With Integrated Matching Network}, year = {2026}, pages = {1-1}, doi = {10.1109/ACCESS.2026.3676131} }

Accelerating Resource Allocation in Open RAN Slicing via Deep Reinforcement Learning

Truong, Nguyen, Luu, Vo, Nguyen, Kavehmadavani & Chatzinotas
IEEE Transactions on Network and Service Management

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The transition to beyond-fifth-generation (B5G) wireless systems has revolutionized cellular networks, driving unprecedented demand for high-bandwidth, ultra low-latency, and massive connectivity services. The open radio access network (Open RAN) and network slicing provide B5G with greater flexibility and efficiency by enabling tailored virtual networks on shared infrastructure. However, managing resource allocation in these frameworks has become increasingly complex. This paper addresses the challenge of optimizing resource allocation across virtual network functions (VNFs) and network slices, aiming to maximize the total reward for admitted slices while minimizing associated costs. By adhering to the Open RAN architecture, we decompose the formulated problem into two subproblems solved at different timescales. Initially, the successive convex approximation (SCA) method is employed to achieve at least a locally optimal solution. To handle the high complexity of binary variables and adapt to time-varying network conditions, traffic patterns, and service demands, we propose a deep reinforcement learning (DRL) approach for real-time and autonomous optimization of resource allocation. Extensive simulations demonstrate that the DRL framework quickly adapts to evolving network environments, significantly improving slicing performance. The results highlight DRL’s potential to enhance resource allocation in future wireless networks, paving the way for smarter, self-optimizing systems capable of meeting the diverse requirements of modern communication services.

@article{11397754, author = {Tuan-Vu Truong and Van-Dinh Nguyen and Quang-Trung Luu and Phi-Son Vo and Xuan-Phu Nguyen and Fatemeh Kavehmadavani and Symeon Chatzinotas}, journal = {IEEE Transactions on Network and Service Management}, title = {Accelerating Resource Allocation in Open RAN Slicing via Deep Reinforcement Learning}, year = {2026}, volume = {23}, pages = {3055-3070}, doi = {10.1109/TNSM.2026.3665553} }

Deadline-Aware Task Offloading with Concurrency in Serverless Edge Computing

Nguyen, Luu, Son, Tran & Nguyen
IEEE Internet of Things Journal

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Serverless edge computing enables low-latency Internet of Things (IoT) services but faces scalability challenges due to complex concurrency and resource management. While existing approaches address function initialization and edge-cloud offloading, they often overlook the joint optimization of serverless concurrency and physical-layer resources, leading to potential service degradation and increased costs. To tackle this, we propose OPLA, a novel cross-layer framework for joint latency and concurrency optimization, designed to minimize end-to-end latency while optimizing concurrent serverless functions. OPLA models interactions between physical-layer resources (e.g., bandwidth, transmission power, offloading ratios) and application-layer concurrency decisions. The formulated problem is a highly non-convex mixed-integer nonlinear program (MINLP), which we prove to be at least NP-complete in certain cases. To approximate its optimal solution efficiently, we propose an iterative exploration-exploitation procedure (EEP). The exploration phase, which is embarrassingly parallelizable, balances solution quality and efficiency with single parameter tuning. The exploitation phase is just a simple successive convex approximation to OPLA. Moreover, we also develop a presolve-postsolve heuristic with deterministic rounding to ensure feasibility for OPLA. Numerical results demonstrate that EEP consistently achieves solutions within a 6% optimality gap relative to a global solver across a wide range of network scales and workloads, confirming its effectiveness and scalability for real-world serverless edge deployments.

@article{11396962, author = {Minh-Tuong Nguyen and Quang-Trung Luu and Vo Phi Son and Le-Nam Tran and Van-Dinh Nguyen}, journal = {IEEE Internet of Things Journal}, title = {Deadline-Aware Task Offloading with Concurrency in Serverless Edge Computing}, year = {2026}, pages = {1-1}, doi = {10.1109/JIOT.2026.3665108} }

Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty

Tuan, Nguyen, Nguyen, Trung, Huynh, Hoang, Krunz & Dutkiewicz
IEEE Transactions on Communications

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Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eaves-dropper (Eve) locations. Unlike conventional ISAC-based friendly jamming (FJ) approaches that require Eve’s CSI or precise angle-of-arrival (AoA) estimates, our method exploits radar echo feedback to guide directional jamming without explicit Eve’s information. To enhance robustness to radar sensing uncertainty, we propose a radar-aware neural network that jointly optimizes beamforming and jamming by integrating a novel nonparametric Fisher Information Matrix (FIM) estimator based on f-divergence. The jamming design satisfies the Cramér–Rao lower bound (CRLB) constraints even in the presence of noisy AoA. For efficient implementation, we introduce a quantized tensor train-based encoder that reduces the model size by more than 100 times with negligible performance loss. We also integrate a non-overlapping secure scheme into the proposed framework, in which specific sub-bands can be dedicated solely to communication. Extensive simulations demonstrate that the proposed solution achieves significant improvements in secrecy rate, reduced block error rate (BLER), and strong robustness against CSI uncertainty and angular estimation errors, under-scoring the effectiveness of the proposed deep learning–driven friendly jamming framework under practical ISAC impairments.

@article{11442290, author = {Bui Minh Tuan and Van-Dinh Nguyen and Diep N. Nguyen and Nguyen Linh Trung and Nguyen Van Huynh and Dinh Thai Hoang and Marwan Krunz and Eryk Dutkiewicz}, journal = {IEEE Transactions on Communications}, title = {Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty}, year = {2026}, pages = {1-1}, doi = {10.1109/TCOMM.2026.3675471} }

Design of High-Efficiency Octave-Bandwidth Single-Diode Rectifier Using Synthesized Elliptic Low-Pass Matching Network

Nguyen, Vuong & Nguyen
IEEE Microwave and Wireless Technology Letters

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This letter presents a novel methodology for designing a compact flat- and high-efficiency single-diode rectifier within an octave bandwidth for low-power energy harvesting (EH) systems. The proposed structure consists of a $1:n$ transformer and a broadband elliptic low-pass filter. The former reduces diode impedance magnitude, which facilitates fundamental wideband matching with low insertion (IL) loss. Meanwhile, the latter with its sharp roll-off characteristic is manipulated to provide efficient fundamental matching and harmonic suppression simultaneously across the entire bandwidth, maintaining uniform optimal efficiency. With this design concept, a low-power rectifier over a 0.7–1.3-GHz bandwidth is designed and prototyped for verification, showing that an optimal power conversion efficiency (PCE) above 60% with a peak of 72.1% is sustained from 0.67 to 1.3 GHz at a 2-dBm input power, corresponding to a fractional bandwidth (FBW) of 63.9%. Also, an average PCE of 68.2% is recorded across the target bandwidth with only a small standard deviation of 2.7%, indicating a stable broadband operation. Furthermore, the proposed rectifier has a miniature size of $26\times 12$ mm.

@article{11338813, author = {Dang-An Nguyen and Dat Vuong and Van-Dinh Nguyen}, journal = {IEEE Microwave and Wireless Technology Letters}, title = {Design of High-Efficiency Octave-Bandwidth Single-Diode Rectifier Using Synthesized Elliptic Low-Pass Matching Network}, year = {2026}, pages = {1-4}, doi = {10.1109/LMWT.2025.3650115} }

Energy Efficiency for Massive MIMO Integrated Sensing and Communication Systems

Nguyen, Nguyen, Nguyen, Luong, Bao, Ngo, Niyato & Chatzinotas
IEEE Journal on Selected Areas in Communications

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This paper explores the energy efficiency (EE) of integrated sensing and communication (ISAC) systems employing massive multiple-input multiple-output (mMIMO) techniques to leverage spatial beamforming gains for both communication and sensing. We focus on an mMIMO-ISAC system operating in an orthogonal frequency-division multiplexing setting with a uniform planar array, zero-forcing downlink transmission, and mono-static radar sensing to exploit multi-carrier channel diversity. By deriving closed-form expressions for the achievable communication rate and Cramér-Rao bounds (CRBs), we are able to determine the overall EE in closed-form. A power allocation problem is then formulated to maximize the system’s EE by balancing communication and sensing efficiency while satisfying communication rate requirements and CRB constraints. Through a detailed analysis of CRB properties, we reformulate the problem into a more manageable form and leverage Dinkelbach’s and successive convex approximation (SCA) techniques to develop an efficient iterative algorithm. A novel initialization strategy is also proposed to ensure high-quality feasible starting points for the iterative optimization process. Extensive simulations demonstrate the significant performance improvement of the proposed approach over baseline approaches. Results further reveal that as communication spectral efficiency rises, the influence of sensing EE on the overall system EE becomes more pronounced, even in sensing-dominated scenarios. Specifically, in the high $\omega $ regime of $2 \times 10^{-3}$ , we observe a 16.7% reduction in overall EE when spectral efficiency increases from 4 to 8 bps/Hz, despite the system being sensing-dominated.

@article{11168825, author = {Huy T. Nguyen and Van-Dinh Nguyen and Nhan Thanh Nguyen and Nguyen Cong Luong and Vo-Nguyen Quoc Bao and Hien Quoc Ngo and Dusit Niyato and Symeon Chatzinotas}, journal = {IEEE Journal on Selected Areas in Communications}, title = {Energy Efficiency for Massive MIMO Integrated Sensing and Communication Systems}, year = {2026}, volume = {44}, pages = {165-180}, doi = {10.1109/JSAC.2025.3610821} }

Network Slice Embedding With Flexible Configurations in 5G Networks and Beyond

Luu, Tran, Nguyen, Kieffer, Hoang, Nguyen, Nguyen & Nguyen
IEEE Networking Letters

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Network slicing enables the creation of multiple virtual networks (i.e., slices) over a shared network infrastructure, each tailored to a specific service. A key challenge lies in network slice embedding, which maps virtual network functions (VNFs) and links onto the physical network. Unlike prior works that assumed fixed configurations, we design a flexible system that allows for selecting the best configuration for each slice based on current physical resource availability during embedding. This leads to a joint optimization of (i) slice configuration selection (SCS) and (ii) slice admission control and embedding (SACE). To solve this, we propose two approaches: an exact method that formulates the joint SCS-SACE problem as an integer linear program (ILP), and a scalable alternative that decouples the problem, solving SCS via reinforcement learning and SACE via either ILP or a heuristic. Simulation results show that allowing flexible configuration selection improves slice acceptance by up to 10%, enabling more efficient slice deployment in resource-constrained networks.

@article{11347585, author = {Quang-Trung Luu and Do-Minh Tran and Minh-Thanh Nguyen and Michel Kieffer and Dinh Thai Hoang and Tai-Hung Nguyen and Huu-Thanh Nguyen and Van-Dinh Nguyen}, journal = {IEEE Networking Letters}, title = {Network Slice Embedding With Flexible Configurations in 5G Networks and Beyond}, year = {2026}, pages = {1-1}, doi = {10.1109/LNET.2026.3653831} }

Optimizing Mixed FSO-RF Downlink Systems With Active RIS and SLIPT-Enabled UAV-BSs

Vu, Dang, Moon, Shin & Nguyen
IEEE Transactions on Communications

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This paper investigates the integration of active reconfigurable intelligent surfaces (ARISs) with uncrewed aerial vehicles (UAVs) in a mixed free-space optics radio frequency (FSO-RF) downlink communication system, enabling simultaneous lightwave information and power transfer (SLIPT). The proposed architecture addresses key challenges in UAV-based networks, including limited endurance and backhaul constraints, by allowing the UAV to harvest energy from the optical backhaul while transmitting RF signals enhanced via ARIS to ground users. The system design aims to maximize the minimum achievable rate among users by jointly optimizing the UAV’s beamforming strategy, 3D placement, ARIS reflection coefficients, optical ground station (OGS) transmit power and the power splitting (PS) ratio at the UAV. An alternating optimization framework is developed to decompose the resulting non-convex problem into efficiently solvable subproblems using inner approximation techniques. Simulation results confirm that the proposed approach significantly outperforms baseline schemes, such as passive RIS, fixed UAV deployment, and static PS configurations, delivering improved rate fairness and energy efficiency. These results demonstrate the potential of ARIS-assisted SLIPT-enabled UAVs to support robust and sustainable downlink communications in next-generation wireless networks.

@article{11358911, author = {Binh-Minh Vu and Ngoc T. Dang and Sangmi Moon and Oh-Soon Shin and Van-Dinh Nguyen}, journal = {IEEE Transactions on Communications}, title = {Optimizing Mixed FSO-RF Downlink Systems With Active RIS and SLIPT-Enabled UAV-BSs}, year = {2026}, volume = {74}, pages = {3600-3616}, doi = {10.1109/TCOMM.2026.3655761} }

Resource Allocation for RIS-Enhanced OFDM-MIMO ISAC Systems

Zivuku, Nguyen, Nguyen, Ntontin, Chatzinotas & Ottersten
IEEE Transactions on Communications

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Integrated sensing and communications (ISAC) has emerged as a key enabler for 6G and beyond. However, ISAC systems face significant challenges, including the sensing function that introduces interference and degrades communication performance, as well as high sensing power consumption that reduces overall communication efficiency, particularly in complex urban environments. To address these issues, we propose a reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) ISAC system, where a RIS enhances connectivity for users in localized coverage gaps. We formulate and study two optimization problems: 1) maximizing system sum spectral efficiency and 2) maximizing global energy efficiency, by jointly optimizing transmit precoding, subcarrier allocation, and RIS phase shifts under power, quality of service, and sensing accuracy constraints. These problems are classified as mixed-integer nonlinear programs, which are generally difficult to solve optimally. To tackle this, we develop efficient iterative algorithms leveraging successive convex approximation, alternating optimization, Riemannian manifolds, and Dinkelbach’s method to obtain at least locally optimal solutions. Simulation results validate the effectiveness of the proposed designs, demonstrating their superiority over benchmark schemes, achieving up to 40% higher spectral efficiency and up to 60% improvement in energy efficiency compared to conventional overlap and random-phase approaches.

@article{11268332, author = {Progress Zivuku and Van-Dinh Nguyen and Nhan Thanh Nguyen and Konstantinos Ntontin and Symeon Chatzinotas and Bj{\"o}rn Ottersten}, journal = {IEEE Transactions on Communications}, title = {Resource Allocation for RIS-Enhanced OFDM-MIMO ISAC Systems}, year = {2026}, volume = {74}, pages = {1777-1792}, doi = {10.1109/TCOMM.2025.3637097} }

SC-GIR: Goal-Oriented Semantic Communication via Invariant Representation Learning for Image Transmission

Wanasekara, Nguyen, Wong, Nguyen, Chatzinotas & Dobre
IEEE Transactions on Mobile Computing

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Goal-oriented semantic communication (SC) aims to revolutionize communication systems by transmitting only task-essential information. However, current approaches face challenges such as joint training at transceivers, leading to redundant data exchange and reliance on labeled datasets, which limits their task-agnostic utility. To address these challenges, we propose a novel framework called Goal-oriented Invariant Representation-based SC (SC-GIR) for image transmission. Our framework leverages self-supervised learning to extract an invariant representation that encapsulates crucial information from the source data, independent of the specific downstream task. This compressed representation facilitates efficient communication while retaining key features for successful downstream task execution. Focusing on machine-to-machine tasks, we utilize covariance-based contrastive learning techniques to obtain a latent representation that is both meaningful and semantically dense. To evaluate the effectiveness of the proposed scheme on downstream tasks, we apply it to various image datasets for lossy compression. The compressed representations are then used in a goal-oriented AI task. Extensive experiments on several datasets demonstrate that SC-GIR outperforms baseline schemes by nearly 10%, and achieves over 85% classification accuracy for compressed data under different SNR conditions. These results underscore the effectiveness of the proposed framework in learning compact and informative latent representations.

@article{11129868, author = {Senura Hansaja Wanasekara and Van-Dinh Nguyen and Kok-Seng Wong and M.-Duong Nguyen and Symeon Chatzinotas and Octavia A. Dobre}, journal = {IEEE Transactions on Mobile Computing}, title = {SC-GIR: Goal-Oriented Semantic Communication via Invariant Representation Learning for Image Transmission}, year = {2026}, volume = {25}, number = {2}, pages = {1483-1498}, doi = {10.1109/TMC.2025.3600434}, month = {Feb} }

TinySense: Effective CSI Compression for Scalable and Accurate Wi-Fi Sensing

Gian, Tran, Pham, Restuccia & Nguyen
IEEE PerCom

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With the growing demand for device-free and privacy-preserving sensing solutions, Wi-Fi sensing has emerged as a promising approach for human pose estimation (HPE). However, existing methods often process vast amounts of channel state information (CSI) data directly, ultimately straining networking resources. This paper introduces TinySense, an efficient compression framework that enhances the scalability of Wi-Fi-based human sensing. Our approach is based on a new vector quantization-based generative adversarial network (VQGAN). Specifically, by leveraging a VQGAN-learned codebook, TinySense significantly reduces CSI data while maintaining the accuracy required for reliable HPE. To optimize compression, we employ the K-means algorithm to dynamically adjust compression bitrates to cluster a large-scale pre-trained codebook into smaller subsets. Furthermore, a Transformer model is incorporated to mitigate bitrate loss, enhancing robustness in unreliable networking conditions. We prototype TinySense on an experimental testbed using Jetson Nano and Raspberry Pi to measure latency and network resource use. Extensive results demonstrate that TinySense significantly outperforms state-of-the-art compression schemes, achieving up to 1.5x higher HPE accuracy score (PCK20) under the same compression rate. It also reduces latency and networking overhead, respectively, by up to 5x and 2.5x. The code repository is available online at here.

@inproceedings{gian2026tinysensee, title = {TinySense: Effective CSI Compression for Scalable and Accurate Wi-Fi Sensing}, author = {Toan Gian and Dung T. Tran and Viet Quoc Pham and Francesco Restuccia and Van-Dinh Nguyen}, year = {2026}, booktitle = {IEEE PerCom}, doi = {https://doi.org/10.48550/arXiv.2601.15838}, url = {https://arxiv.org/abs/2601.15838} }

2025

A Metaheuristic Approach for Mission Assignment and Task Offloading in Open RAN-Enabled Intelligent Transport Systems

Nguyen, Van Thieu, Luu, Son & Nguyen
GLOBECOM 2025 - 2025 IEEE Global Communications Conference

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We explore mission assignment and task offloading in Open Radio Access Network (Open RAN)-enabled intelligent transportation systems (ITS), where autonomous vehicles utilize mobile edge computing for efficient processing. Existing studies often overlook mission dependencies and offloading costs, leading to suboptimal decisions. To address this, we formulate a novel optimization problem that integrates these factors and enhances performance through vehicle cooperation. We then develop the chaotic Gaussian-based global artificial rabbit optimization (CG-GARO) algorithm, a new metaheuristic approach, which significantly improves mission assignment efficiency and resource utilization. Simulation results show that our approach surpasses baseline metaheuristics in both system benefits and mission completion rates, demonstrating strong potential for real-world deployment in dynamic ITS environments.

@inproceedings{11431892, author = {Ngoc Hung Nguyen and Nguyen {Van Thieu} and Quang-Trung Luu and Vo-Phi Son and Van-Dinh Nguyen}, booktitle = {GLOBECOM 2025 - 2025 IEEE Global Communications Conference}, title = {A Metaheuristic Approach for Mission Assignment and Task Offloading in Open RAN-Enabled Intelligent Transport Systems}, year = {2025}, pages = {811-816}, doi = {10.1109/GLOBECOM59602.2025.11431892}, month = {Dec} }

Adaptive Task Scheduling under Hard Deadlines in Edge Environments Using Deep Reinforcement Learning

Nguyen, Van Thieu, Pham & Nguyen
2025 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)

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Dynamic queuing and random task arrivals pose significant challenges for modern computing systems, especially in environments where strict adherence to hard deadlines is critical. With the growing demand for real-time inference in artificial intelligence (AI) applications, optimizing task scheduling at the network edge has become increasingly vital. In this paper, we introduce a novel task scheduling framework that combines deep reinforcement learning (DRL) with multiple queuing algorithms to efficiently manage incoming tasks under hard deadline constraints. Our approach enables the dynamic learning of optimal scheduling policies in heterogeneous edge environments. Through comprehensive evaluations across various queuing strategies, we demonstrate that our DRL-based scheduler consistently outperforms baseline methods in both deadline success rate and overall system efficiency. This work underscores the importance of adaptive, intelligent decision-making in time-sensitive edge computing and positions DRL as a promising paradigm for next-generation scheduling solutions.

@inproceedings{11137627, author = {Ngoc Hung Nguyen and Nguyen {Van Thieu} and Minh-Hoang Pham and Van-Dinh Nguyen}, booktitle = {2025 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)}, title = {Adaptive Task Scheduling under Hard Deadlines in Edge Environments Using Deep Reinforcement Learning}, year = {2025}, pages = {1-5}, doi = {10.1109/ITC-CSCC66376.2025.11137627}, month = {July} }

Energy-Aware Resource Allocation for Energy Harvesting Powered Wireless Sensor Nodes

Ngo, Nguyen, Nguyen & Nguyen
IEEE Communications Letters

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Low harvested energy poses a significant challenge to sustaining continuous communication in energy harvesting (EH)-powered wireless sensor networks. This is mainly due to intermittent and limited power availability from radio frequency signals. In this letter, we introduce a novel energy-aware resource allocation problem aimed at enabling the asynchronous accumulate-then-transmit protocol, offering an alternative to the extensively studied harvest-then-transmit approach. Specifically, we jointly optimize power allocation and time fraction dedicated to EH to maximize the average long-term system throughput, accounting for both data and energy queue lengths. By leveraging inner approximation and network utility maximization techniques, we develop a simple yet efficient iterative algorithm that guarantees at least a local optimum and achieves long-term utility improvement. Numerical results highlight the proposed approach’s effectiveness in terms of both queue length and sustained system throughput.

@article{10841387, author = {Ngoc M. Ngo and Trung T. Nguyen and Phuc H. Nguyen and Van-Dinh Nguyen}, journal = {IEEE Communications Letters}, title = {Energy-Aware Resource Allocation for Energy Harvesting Powered Wireless Sensor Nodes}, year = {2025}, volume = {29}, number = {3}, pages = {542-546}, doi = {10.1109/LCOMM.2025.3529729}, month = {March} }

Intelligent User Association and Scheduling in Open RAN: A Hierarchical Optimization Framework

Kavehmadavani, Vu, Nguyen & Chatzinotas
IEEE Transactions on Communications

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In the ever-evolving landscape of NextG wireless networks, Open radio access network (RAN) emerges as a transformative paradigm, revolutionizing network architectures and fostering innovation through its open, intelligent and disaggregated approach. By integrating RAN intelligent controllers (RICs), we can seamlessly implement machine learning (ML) algorithms to cater to diverse vertical applications and deployment environments without the need for intricate planning. However, this architecture suffers from two critical challenges: frequent handovers and load balancing amid varying traffic demands of different services in dynamic environments. To address these issues, this study proposes a joint intelligent user association, congestion control, and resource scheduling (IUCR) scheme. Aligning with the 7.2x functional split (FS) option recommended by the O-RAN Alliance, we present a hierarchical optimization framework incorporating heuristic methods, successive convex approximation (SCA), and a distributed deep reinforcement learning (DRL) approach across different Open RAN components, such as RICs and RAN layers. The simulation results convincingly demonstrate the superior performance of the proposed scheme compared to centralized approaches, validating its effectiveness.

@article{11095741, author = {Fatemeh Kavehmadavani and Thang X. Vu and Van-Dinh Nguyen and Symeon Chatzinotas}, journal = {IEEE Transactions on Communications}, title = {Intelligent User Association and Scheduling in Open RAN: A Hierarchical Optimization Framework}, year = {2025}, volume = {73}, number = {11}, pages = {11574-11589}, doi = {10.1109/TCOMM.2025.3592584}, month = {Nov} }

Latency Optimization for Wireless Federated Learning in Multihop Networks

Shaon, Nguyen & Nguyen
IEEE Transactions on Vehicular Technology

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In this paper, we study a novel latency minimization problem in wireless federated learning (FL) across multi-hop networks. The system comprises multiple routes, each integrating leaf and relay nodes for FL model training. We explore a personalized learning and adaptive aggregation-aware FL (PAFL) framework that effectively addresses data heterogeneity across participating nodes by harmonizing individual and collective learning objectives. We formulate an optimization problem aimed at minimizing system latency through the joint optimization of leaf and relay nodes, as well as relay routing indicator. We also incorporate an additional energy harvesting scheme for the relay nodes to help with their relay tasks. This formulation presents a computationally demanding challenge, and thus we develop a simple yet efficient algorithm based on block coordinate descent and successive convex approximation (SCA) techniques. Simulation results illustrate the efficacy of our proposed joint optimization approach for leaf and relay nodes with relay routing indicator. We observe significant latency savings in the wireless multi-hop PAFL system, with reductions of up to 69.37% compared to schemes optimizing only one node type, traditional greedy algorithm, and scheme without relay routing indicator.

@article{11029176, author = {Shaba Shaon and Van-Dinh Nguyen and Dinh C. Nguyen}, journal = {IEEE Transactions on Vehicular Technology}, title = {Latency Optimization for Wireless Federated Learning in Multihop Networks}, year = {2025}, volume = {74}, number = {11}, pages = {18318-18323}, doi = {10.1109/TVT.2025.3577530}, month = {Nov} }

Metaheuristic Optimization of Trajectory and Dynamic Time Splitting for UAV Communication Systems

Van Chien, Minh Quan, Shin & Nguyen
IEEE Communications Letters

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The integration of unmanned aerial vehicles (UAVs) into wireless communication systems has emerged as a transformative approach, promising cost-efficient connectivity. This letter addresses the optimization of the dynamic time-splitting ratio and flight trajectory for a communication system linking a ground base station to the UAV equipped with backscatter devices (referred to as UB), and from UB to an end user. Given the inherent non-convexity of the problem, we develop two meta-heuristic-based approaches inspired by genetic algorithm and particle swarm optimization to enhance the total achievable rate while reducing computational complexity. Numerical results demonstrate the effectiveness of these meta-heuristic solutions, showcasing significant improvements in the achievable rate and computation time compared to existing benchmarks.

@article{10947208, author = {Trinh {Van Chien} and Nguyen {Minh Quan} and Oh-Soon Shin and Van-Dinh Nguyen}, journal = {IEEE Communications Letters}, title = {Metaheuristic Optimization of Trajectory and Dynamic Time Splitting for UAV Communication Systems}, year = {2025}, volume = {29}, number = {6}, pages = {1200-1204}, doi = {10.1109/LCOMM.2025.3556714}, month = {June} }

Multi-Modal Human Pose Estimation: A Wi-Fi-Driven Approach with Adaptive Kernel Selection

Gian, Tran, Pham, Tran & Nguyen
IEEE Transactions on Artificial Intelligence

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Wireless Fidelity (Wi-Fi)-based human pose estimation (HPE) has emerged as a promising alternative to vision-based techniques, enabling human pose detection and movement interpretation while ensuring privacy. However, high computational costs and performance limitations hinder widespread adoption, particularly on resource-constrained devices. This paper introduces HPE-Li++, a novel approach leveraging multi-modal sensors (e.g., camera and Wi-Fi) to achieve accurate 3D skeletal HPE with lightweight computation.We develop an efficient deep neural network featuring a multi-branch convolutional neural network (CNN) enhanced by selective kernel attention (SKA), which dynamically adjusts kernel sizes based on input characteristics, improving adaptability with negligible complexity increase. To enhance efficiency and robustness, we incorporate a Transformer module to capture inter-domain correlations for effective feature extraction and a stacked autoencoder (SAE)-based denoiser to improve accuracy through latent representations while reducing computational cost. Extensive experiments on MM-Fi andWiPose datasets demonstrate that HPE-Li++ outperforms state-of-the-art methods, achieving 85.58% and 94.27% at PCK50, respectively, with minimal computational overhead. Notably, it remains robust under noise, maintaining 80% PCK50 under AWGN noise with an error variance of 0.5.

@article{11242148, author = {Toan D. Gian and Dung T. Tran and Quoc-Viet Pham and Le-Nam Tran and Van-Dinh Nguyen}, journal = {IEEE Transactions on Artificial Intelligence}, title = {Multi-Modal Human Pose Estimation: A Wi-Fi-Driven Approach with Adaptive Kernel Selection}, year = {2025}, pages = {1-14}, doi = {10.1109/TAI.2025.3631005} }

Performance Analysis and Power Allocation for Massive MIMO ISAC Systems

Nguyen, Nguyen, Nguyen, Ngo, Swindlehurst & Juntti
IEEE Transactions on Signal Processing

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Integrated sensing and communications (ISAC) is envisioned as a key feature in future wireless communications networks. Its integration with massive multiple-input-multiple-output (MIMO) techniques promises to leverage substantial spatial beamforming gains for both functionalities. In this work, we consider a massive MIMO-ISAC system employing a uniform planar array with zero-forcing and maximum-ratio downlink transmission schemes combined with monostatic radar-type sensing. Our focus lies on deriving closed form expressions for the achievable communications rate and the Cramér–Rao lower bound (CRLB), which serve as performance metrics for communications and sensing operations, respectively. The expressions enable us to investigate important operational characteristics of massive MIMO-ISAC, including the mutual effects of communications and sensing as well as the advantages stemming from using a very large antenna array for each functionality. Furthermore, we devise a power allocation strategy based on successive convex approximation to maximize the communications rate while guaranteeing the CRLB constraints and transmit power budget. Extensive numerical results are presented to validate our theoretical analyses and demonstrate the efficiency of the proposed power allocation approach.

@article{10938928, author = {Nhan Thanh Nguyen and Van-Dinh Nguyen and Hieu V. Nguyen and Hien Quoc Ngo and A. Lee Swindlehurst and Markku Juntti}, journal = {IEEE Transactions on Signal Processing}, title = {Performance Analysis and Power Allocation for Massive MIMO ISAC Systems}, year = {2025}, volume = {73}, pages = {1691-1707}, doi = {10.1109/TSP.2025.3554012} }

Robust WiFi Sensing-Based Human Pose Estimation Using Denoising Autoencoder and CNN With Dynamic Subcarrier Attention

Hoang Nguyen, Nguyen, Luu, Dinh Gian & Shin
IEEE Internet of Things Journal

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WiFi sensing-based human pose estimation (HPE) has gained significant attention in the academic community due to its advantages over vision- and sensor-based methods, including nonintrusiveness, convenience, and enhanced privacy protection. However, most existing WiFi-based pose Estimators suffer from poor performance and lack robustness in the presence of random noise. To address these challenges, this article presents a novel HPE architecture comprising two key modules: 1) a Denoiser and 2) an Estimator. The Denoiser is based on an autoencoder structure, while the Estimator is based on a new convolutional neural network (CNN) called SDy-CNN, which is designed to dynamically focus on high-information subcarriers of orthogonal frequency division multiplexing signals. Additionally, Bayesian optimization is employed to fine-tune the architecture’s parameters for optimal performance flexibly. Experiments conducted on a comprehensive dataset, MM-Fi, demonstrate that the proposed architecture significantly outperforms existing state-of-the-art Estimators, achieving up to an 8.38% improvement in HPE accuracy in clean data scenarios and up to a 14% improvement in noisy data scenarios. It has also been proven to gain computational efficiency when being much faster than other methods.

@article{10855560, author = {Xuan {Hoang Nguyen} and Van-Dinh Nguyen and Quang-Trung Luu and Toan {Dinh Gian} and Oh-Soon Shin}, journal = {IEEE Internet of Things Journal}, title = {Robust WiFi Sensing-Based Human Pose Estimation Using Denoising Autoencoder and CNN With Dynamic Subcarrier Attention}, year = {2025}, volume = {12}, number = {11}, pages = {17066-17079}, doi = {10.1109/JIOT.2025.3535156}, month = {June} }

Secure Multiuser Communications with Stacked Intelligent Metasurfaces using Quantum Reinforcement Learning

Hoang, Pham, Luu & Nguyen
2025 International Conference on Advanced Technologies for Communications (ATC)

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In this paper, we investigate a secure multiple-input single-output communication system empowered by stacked intelligent metasurfaces, comprising a base station, multiple users, and an eavesdropper. The base station leverages stacked intelligent metasurfaces to enhance physical layer security. Specifically, we consider a practical scenario where the BS has imperfect knowledge of the eavesdropper’s channel, introducing uncertainty into the system. Under this condition, the goal is to jointly optimize power allocation and stacked intelligent metasurfaces phase shifts to maximize the average secrecy rate across users. This optimization problem is highly non-convex and complicated further due to the dynamic wireless environment and channel state information uncertainty. To tackle these challenges, we propose a quantum reinforcement learning approach that learns optimal power and phase configurations. Simulation results demonstrate that the proposed QRL-based method significantly outperforms conventional baselines in terms of both adaptability and secrecy performance.

@inproceedings{11268562, author = {Le-Hung Hoang and Minh-Hoang Pham and Quang-Trung Luu and Van-Dinh Nguyen}, booktitle = {2025 International Conference on Advanced Technologies for Communications (ATC)}, title = {Secure Multiuser Communications with Stacked Intelligent Metasurfaces using Quantum Reinforcement Learning}, year = {2025}, pages = {1-6}, doi = {10.1109/ATC67618.2025.11268562}, month = {Oct} }

Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI

Minh Tuan, Nguyen, Linh Trung, Nguyen, Van Huynh, Thai Hoang, Krunz & Dutkiewicz
IEEE Internet of Things Journal

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Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems.

@article{10858310, author = {Bui {Minh Tuan} and Diep N. Nguyen and Nguyen {Linh Trung} and Van-Dinh Nguyen and Nguyen {Van Huynh} and Dinh {Thai Hoang} and Marwan Krunz and Eryk Dutkiewicz}, journal = {IEEE Internet of Things Journal}, title = {Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI}, year = {2025}, volume = {12}, number = {11}, pages = {16009-16022}, doi = {10.1109/JIOT.2025.3536702}, month = {June} }

Wireless Power Transfer Meets Semantic Communication for Resource-Constrained IoT Networks: A Joint Transmission Mode Selection and Resource Management Approach

Sang, Hai, Anh, Cong Luong, Nguyen, Gong, Niyato & In Kim
IEEE Internet of Things Journal

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In this work, we consider the integration of energy harvesting (EH) and semantic communication strategies in resource-constrained Internet of Things (IoT) systems. The system empowers IoT devices to harvest energy from a base station, utilizing this harvested energy for the extraction and transmission of semantic information (e.g., scene graphs). To maximize the total transmission of image data or scene graphs to the central station, we formulate a comprehensive problem that jointly optimizes the EH duration, original image selection, transmit power, and channel allocation to IoT devices. The challenges arising from the dynamic environments and uncertain system parameters are effectively tackled by policy-based deep reinforcement learning algorithms, i.e., advantage actor-critic (A2C) and proximal policy optimization (PPO). Simulation results are implemented on the real data set clearly showing the superior performance achieved by our proposed algorithms compared to the baseline schemes. Notably, our approach enables IoT devices to transmit a greater number of original images and scene graphs with increased triplets to the central station, as highlighted in the simulation outcomes. This phenomenon showcases the potential of our strategy to enhance the capabilities of IoT systems in dynamic environments.

@article{10684702, author = {Nguyen Huu Sang and Nguyen Duc Hai and Nguyen Duc Duy Anh and Nguyen {Cong Luong} and Van-Dinh Nguyen and Shimin Gong and Dusit Niyato and Dong {In Kim}}, journal = {IEEE Internet of Things Journal}, title = {Wireless Power Transfer Meets Semantic Communication for Resource-Constrained IoT Networks: A Joint Transmission Mode Selection and Resource Management Approach}, year = {2025}, volume = {12}, number = {1}, pages = {556-568}, doi = {10.1109/JIOT.2024.3464646}, month = {Jan} }

2024

Deadline-Aware Joint Task Scheduling and Offloading in Mobile-Edge Computing Systems

Nguyen, Nguyen, Nguyen, Thieu, Nguyen & Chatzinotas
IEEE Internet of Things Journal

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The demand for stringent interactive Quality of Service has intensified in both mobile-edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks in these systems necessitates adherence to specific deadlines or achieving extremely low latency. To optimize task scheduling performance, existing research has mainly focused on reducing the number of late jobs whose deadlines are not met. However, the primary challenge with these methods lies in the total search time and scheduling efficiency. In this article, we present the optimal job scheduling algorithm designed to determine the optimal task order for a given set of tasks. In addition, users are enabled to make informed decisions for offloading tasks based on the information provided by servers. The details of performance analysis are provided to show its optimality and low complexity with the linearithmic time $\mathcal {O}(n\log n)$ , where n is the number of tasks. To tackle the uncertainty of the randomly arriving tasks, we further develop an online approach with fast outage detection that achieves rapid acceptance times with time complexity of $\mathcal {O}(n)$ . Extensive numerical results are provided to demonstrate the effectiveness of the proposed algorithm in terms of the service ratio and scheduling cost.

@article{10592058, author = {Ngoc Hung Nguyen and Van-Dinh Nguyen and Anh Tuan Nguyen and Nguyen Van Thieu and Hoang Nam Nguyen and Symeon Chatzinotas}, journal = {IEEE Internet of Things Journal}, title = {Deadline-Aware Joint Task Scheduling and Offloading in Mobile-Edge Computing Systems}, year = {2024}, volume = {11}, number = {20}, pages = {33282-33295}, doi = {10.1109/JIOT.2024.3425854}, month = {Oct} }

Efficient Resource Allocation Framework for Network Slicing-enabled Open RAN

Truong, Luu & Nguyen
2024 Tenth International Conference on Communications and Electronics (ICCE)

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The transition to 5G technology has revolutionized cellular networks, leading to an unprecedented demand for high-bandwidth and low-latency services. To meet these evolving demands, next-generation mobile networks are turning to innovative solutions such as network slicing within the open radio access network (RAN) architecture. This paper focuses on addressing the challenges associated with dynamic virtual network function (VNF) mapping and resource allocation in terms of both communication and computing resources. The ultimate goal is to simultaneously maximize the total reward for admitted slices and minimize the associated cost by allocating resources to the Open RAN slices catering to diverse service types. To efficiently solve the formulated problem, we decompose it into two sub-problems tailored to different timescales which are executed at the different layers within the Open RAN architecture. To solve the non-convex long-term problem, we first transform it to a more tractable form and employ the successive convex approximation (SCA) method to achieve at least a locally optimal solution. Numerical simulations demonstrate the efficacy and quick convergence of the proposed algorithm, highlighting its potential to improve network slicing performance and support diverse applications in future wireless networks.

@inproceedings{10634735, author = {Tuan-Vu Truong and Quang-Trung Luu and Van-Dinh Nguyen}, booktitle = {2024 Tenth International Conference on Communications and Electronics (ICCE)}, title = {Efficient Resource Allocation Framework for Network Slicing-enabled Open RAN}, year = {2024}, pages = {747-752}, doi = {10.1109/ICCE62051.2024.10634735}, month = {July} }

Empowering Traffic Steering in 6G Open RAN With Deep Reinforcement Learning

Kavehmadavani, Nguyen, Vu & Chatzinotas
IEEE Transactions on Wireless Communications

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The sixth-generation (6G) wireless network landscape is evolving toward enhanced programmability, virtualization, and intelligence to support heterogeneous use cases. The O-RAN Alliance is pivotal in this transition, introducing a disaggregated architecture and open interfaces within the 6G network. Our paper explores an intelligent traffic steering (TS) scheme within the Open radio access network (RAN) architecture, aimed at improving overall system performance. Our novel TS algorithm efficiently manages diverse services, improving shared infrastructure performance amid unpredictable demand fluctuations. To address challenges like varying channel conditions, dynamic traffic demands, we propose a multi-layer optimization framework tailored to different timescales. Techniques such as long-short-term memory (LSTM), heuristics, and multi-agent deep reinforcement learning (MADRL) are employed within the non-real-time (non-RT) RAN intelligent controller (RIC). These techniques collaborate to make decisions on a larger timescale, defining custom control applications such as the intelligent TS-xAPP deployed at the near-real-time (near-RT) RIC. Meanwhile, optimization on a smaller timescale occurs at the RAN layer after receiving inferences/policies from RICs to address dynamic environments. The simulation results confirm the system’s effectiveness in intelligently steering traffic through a slice-aware scheme, improving eMBB throughput by an average of 99.42% over slice isolation.

@article{10528242, author = {Fatemeh Kavehmadavani and Van-Dinh Nguyen and Thang X. Vu and Symeon Chatzinotas}, journal = {IEEE Transactions on Wireless Communications}, title = {Empowering Traffic Steering in 6G Open RAN With Deep Reinforcement Learning}, year = {2024}, volume = {23}, number = {10}, pages = {12782-12798}, doi = {10.1109/TWC.2024.3396273}, month = {Oct} }

Encrypted Data Caching and Learning Framework for Robust Federated Learning-Based Mobile Edge Computing

Nguyen, Saputra, Hoang, Nguyen, Nguyen, Xiao & Dutkiewicz
IEEE/ACM Transactions on Networking

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Federated Learning (FL) plays a pivotal role in enabling artificial intelligence (AI)-based mobile applications in mobile edge computing (MEC). However, due to the resource heterogeneity among participating mobile users (MUs), delayed updates from slow MUs may deteriorate the learning speed of the MEC-based FL system, commonly referred to as the straggling problem. To tackle the problem, this work proposes a novel privacy-preserving FL framework that utilizes homomorphic encryption (HE) based solutions to enable MUs, particularly resource-constrained MUs, to securely offload part of their training tasks to the cloud server (CS) and mobile edge nodes (MENs). Our framework first develops an efficient method for packing batches of training data into HE ciphertexts to reduce the complexity of HE-encrypted training at the MENs/CS. On that basis, the mobile service provider (MSP) can incentivize straggling MUs to encrypt part of their local datasets that are uploaded to certain MENs or the CS for caching and remote training. However, caching a large amount of encrypted data at the MENs and CS for FL may not only overburden those nodes but also incur a prohibitive cost of remote training, which ultimately reduces the MSP’s overall profit. To optimize the portion of MUs’ data to be encrypted, cached, and trained at the MENs/CS, we formulate an MSP’s profit maximization problem, considering all MUs’ and MENs’ resource capabilities and data handling costs (including encryption, caching, and training) as well as the MSP’s incentive budget. We then show that the problem is convex and can be efficiently solved using an interior point method. Extensive simulations on a real-world human activity recognition dataset show that our proposed framework can achieve much higher model accuracy (improving up to 24.29%) and faster convergence rate (by 2.86 times) than those of the conventional FedAvg approach when the straggling probability varies between 20% and 80%. Moreover, the proposed framework can improve the MSP’s profit up to 2.84 times compared with other baseline FL approaches without MEN-assisted training.

@article{10438353, author = {Chi-Hieu Nguyen and Yuris Mulya Saputra and Dinh Thai Hoang and Diep N. Nguyen and Van-Dinh Nguyen and Yong Xiao and Eryk Dutkiewicz}, journal = {IEEE/ACM Transactions on Networking}, title = {Encrypted Data Caching and Learning Framework for Robust Federated Learning-Based Mobile Edge Computing}, year = {2024}, volume = {32}, number = {3}, pages = {2705-2720}, doi = {10.1109/TNET.2024.3365815}, month = {June} }

Enhancing Immersion and Presence in the Metaverse With Over-the-Air Brain-Computer Interface

Hieu, Hoang, Nguyen, Nguyen, Xiao & Dutkiewicz
IEEE Transactions on Wireless Communications

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This article proposes a novel framework that utilizes an over-the-air Brain-Computer Interface (BCI) to learn Metaverse users’ expectations. By interpreting users’ brain activities, our framework can optimize physical resources and enhance Quality-of-Experience (QoE) for users. To achieve this, we leverage a Wireless Edge Server (WES) to process electroencephalography (EEG) signals via uplink wireless channels, thus eliminating the computational burden for Metaverse users’ devices. As a result, the WES can learn human behaviors, adapt system configurations, and allocate radio resources to tailor personalized user settings. Despite the potential of BCI, the inherent noisy wireless channels and uncertainty of the EEG signals make the related resource allocation and learning problems especially challenging. We formulate the joint learning and resource allocation problem as a mixed integer programming problem. Our solution involves two algorithms: a hybrid learning algorithm and a meta-learning algorithm. The hybrid learning algorithm can effectively find the solution for the formulated problem. Specifically, the meta-learning algorithm can further exploit the neurodiversity of the EEG signals across multiple users, leading to higher classification accuracy. Extensive simulation results with real-world BCI datasets show the effectiveness of our framework with low latency and high EEG signal classification accuracy.

@article{10706833, author = {Nguyen Quang Hieu and Dinh Thai Hoang and Diep N. Nguyen and Van-Dinh Nguyen and Yong Xiao and Eryk Dutkiewicz}, journal = {IEEE Transactions on Wireless Communications}, title = {Enhancing Immersion and Presence in the Metaverse With Over-the-Air Brain-Computer Interface}, year = {2024}, volume = {23}, number = {12}, pages = {18532-18548}, doi = {10.1109/TWC.2024.3470108}, month = {Dec} }

Fairness Enhancement of UAV Systems With Hybrid Active-Passive RIS

Nguyen, Nguyen, Van Nguyen, Wu, Tölli, Chatzinotas & Juntti
IEEE Transactions on Wireless Communications

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We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38%–63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

@article{10266977, author = {Nhan Thanh Nguyen and Van-Dinh Nguyen and Hieu {Van Nguyen} and Qingqing Wu and Antti T{\"o}lli and Symeon Chatzinotas and Markku Juntti}, journal = {IEEE Transactions on Wireless Communications}, title = {Fairness Enhancement of UAV Systems With Hybrid Active-Passive RIS}, year = {2024}, volume = {23}, number = {5}, pages = {4379-4396}, doi = {10.1109/TWC.2023.3317934}, month = {May} }

HPE-Li: WiFi-Enabled Lightweight Dual Selective Kernel Convolution for Human Pose Estimation

D. Gian, Dac Lai, Van Luong, Wong & Nguyen
European Conference on Computer Vision (ECCV)

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WiFi-based human pose estimation (HPE) has emerged as a promising alternative to conventional vision-based techniques, yet faces the high computational cost hindering its widespread adoption. This paper introduces a novel HPE-Li approach that harnesses multi-modal sensors (e.g. camera and WiFi) to generate accurate 3D skeletal in HPE. We then develop an efficient deep neural network to process raw WiFi signals. Our model incorporates a distinctive multi-branch convolutional neural network (CNN) empowered by a selective kernel attention (SKA) mechanism. Unlike standard CNNs with fixed receptive fields, the SKA mechanism is capable of dynamically adjusting kernel sizes according to input data characteristics, enhancing adaptability without increasing complexity. Extensive experiments conducted on two MM-Fi and WiPose datasets underscore the superiority of our method over state-of-the-art approaches, while ensuring minimal computational overhead, rendering it highly suitable for large-scale scenarios.

@inproceedings{d2024hpe, title = {HPE-Li: WiFi-Enabled Lightweight Dual Selective Kernel Convolution for Human Pose Estimation}, author = {Toan {D. Gian} and Tien {Dac Lai} and Thien {Van Luong} and Kok-Seng Wong and Van-Dinh Nguyen}, booktitle = {European Conference on Computer Vision (ECCV)}, pages = {93--111}, year = {2024}, doi = {https://doi.org/10.1007/9} }

Incentive Mechanism and Semantic Communication for Edge Computing-Assisted Metaverse

Luong, Huynh-The, Nguyen, Ng, Chatzinotas, Niyato & Pham
IEEE Network

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Edge computing as a disruptive solution addresses the emerging requirement of low latency data processing in Metaverse. However, edge computing resources are provided by computing service providers (ECPs) and thus it is essential to design appealing incentive mechanisms for the provision of limited resources. Meanwhile, deep learning (DL)-based auction has recently been proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality (IR) and incentive compatibility (IC). Therefore, in this work, we introduce the design of the DL-based auction for the computing resource allocation for the edge computing-assisted Metaverse. Furthermore, a semantic communication (SemCom) technique is exploited that helps to reduce the offloading data and offloading cost for the VSPs. Particularly, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present edge computing, incentive mechanisms, and SemCom. Third, we present the design of the DL-based auction for edge resource allocation for the edge computing-assisted Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue of the ECP and that VSPs pay a lower offloading cost with SemCom.

@article{10322742, author = {Nguyen Cong Luong and Thien Huynh-The and Van-Dinh Nguyen and Derrick Wing Kwan Ng and Symeon Chatzinotas and Dusit Niyato and Quoc-Viet Pham}, journal = {IEEE Network}, title = {Incentive Mechanism and Semantic Communication for Edge Computing-Assisted Metaverse}, year = {2024}, volume = {38}, number = {3}, pages = {277-284}, doi = {10.1109/MNET.2023.3334285}, month = {May} }

Joint RIS-Aided Precoding and Multislot Scheduling for Maximum User Admission in Smart Cities

Zivuku, Kisseleff, Nguyen, Martins, Ntontin, Chatzinotas & Ottersten
IEEE Transactions on Communications

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Reconfigurable intelligent surfaces (RISs) have emerged as a game-changing technology to improve wireless network performance by intelligently manipulating and customizing the physical propagation environment. Such capability is especially important for the application of smart cities as it increases wireless service offers and quality to end-users. In this paper, we aim to maximize the number of served users in a challenging RIS-aided smart city street by jointly optimizing the multislot scheduling, precoding, and passive RIS-based beamforming design under quality of service and power constraints. Multislot scheduling is introduced in order to benefit from additional time diversity and thus better exploit the available degrees of freedom. The formulated problem is a mixed integer nonlinear programming, which is NP-hard. To solve the problem with affordable complexity, we develop an efficient iterative algorithm based on binary variable relaxation, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate the superiority of the proposed design over the design without RIS and the design without scheduling, especially in the presence of a large number of users. In addition, results illustrate that by introducing a quality of service margin, the proposed design can improve its robustness to outdated channel state information in mobility scenarios.

@article{10271702, author = {Progress Zivuku and Steven Kisseleff and Van-Dinh Nguyen and Wallace A. Martins and Konstantinos Ntontin and Symeon Chatzinotas and Bj{\"o}rn Ottersten}, journal = {IEEE Transactions on Communications}, title = {Joint RIS-Aided Precoding and Multislot Scheduling for Maximum User Admission in Smart Cities}, year = {2024}, volume = {72}, number = {1}, pages = {418-433}, doi = {10.1109/TCOMM.2023.3321731}, month = {Jan} }

Lossy Compression of Multi-Channel EEG and PPG Signals Based on Golomb-Rice Coding with Parameter Estimation

Wanasekara, Dung, Nguyen & Nguyen
2024 International Conference on Advanced Technologies for Communications (ATC)

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The integration of electroencephalography (EEG) and photoplethysmography (PPG) in consumer-grade wearable devices is revolutionizing healthcare by enabling continuous health monitoring. However, these devices face significant challenges in terms of power consumption, computational complexity, and data storage, necessitating efficient data compression techniques. This paper proposes a modified Golomb-Rice coding method tailored for lossy compression of multi-channel EEG and PPG signals. By leveraging the statistical properties of filtered data and employing an optimized parameter estimation strategy, the proposed method achieves high compression ratios while minimizing computational overhead and preserving critical signal information. The proposed algorithm shifts data to handle negative values, scales and converts it into an appropriate format for encoding, and uses a novel parameter estimation technique to optimize the Golomb-Rice parameter. Extensive simulations on EEG and PPG datasets demonstrate that this parameter estimation method yields significantly better performance, improving both efficiency and processing speed. Results underscore the importance of preprocessing in data compression and provide a robust framework for future advancements in biomedical signal processing.

@inproceedings{10908307, author = {Senura Hansaja Wanasekara and Han Huy Dung and Ngoc Hung Nguyen and Van-Dinh Nguyen}, booktitle = {2024 International Conference on Advanced Technologies for Communications (ATC)}, title = {Lossy Compression of Multi-Channel EEG and PPG Signals Based on Golomb-Rice Coding with Parameter Estimation}, year = {2024}, pages = {756-761}, doi = {10.1109/ATC63255.2024.10908307}, month = {Oct} }

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

Nguyen, Vu, Nguyen, Nguyen, Juntti, Luong, Hoang, Nguyen & Chatzinotas
IEEE Journal on Selected Areas in Communications

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To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: $i$ ) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; $ii$ ) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and $iii$ ) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

@article{10330565, author = {Van-Dinh Nguyen and Thang X. Vu and Nhan Thanh Nguyen and Dinh C. Nguyen and Markku Juntti and Nguyen Cong Luong and Dinh Thai Hoang and Diep N. Nguyen and Symeon Chatzinotas}, journal = {IEEE Journal on Selected Areas in Communications}, title = {Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework}, year = {2024}, volume = {42}, number = {2}, pages = {389-405}, doi = {10.1109/JSAC.2023.3336183}, month = {Feb} }

Optimal Auction for Effective Energy Management in UAV-Assisted Vehicular Metaverse Synchronization Systems

Luong, Chau, Anh, Sang, Feng, Nguyen, Niyato & In Kim
IEEE Transactions on Vehicular Technology

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In this article, we investigate an effective energy management scheme in a unmanned aerial vehicle (UAV)-assisted vehicular Metaverse synchronization system. UAVs purchase energy resources from an energy service provider (ESP) and collect data for a virtual service provider (VSP) to perform synchronization between physical objects and digital twins (DTs). The key issue is to motivate both ESP and UAVs to participate in the energy trading market. To doing so, we design an incentive mechanism that maximizes the revenue of the ESP while guaranteeing desired economic properties, i.e. individual rationality (IR) and incentive compatibility (IC). In particular, we first consider a single energy unit market, where a deep learning (DL)-based auction scheme is developed to construct neural networks from the analytical results of Myerson auction. The proposed DL-based auction is guaranteed to fulfill the optimal auction. We then consider a general scenario in which ESP has multiple energy units available to UAVs. A novel DL-based auction with feed-forward neural networks (FNNs) is proposed to jointly optimize the energy unit allocation and payment rules. We provide numerical results to demonstrate the performance improvement of the DL-based auction schemes compared to the classical auctions in terms of revenue, IC and IR. In particular, for the single energy unit market, the proposed DL-based auction scheme significantly improves the revenue compared with the classical auction and more interestingly, is able to avoid prevent UAVs from submitting their false values.

@article{10225312, author = {Nguyen Cong Luong and Le Khac Chau and Nguyen Do Duy Anh and Nguyen Huu Sang and Shaohan Feng and Van-Dinh Nguyen and Dusit Niyato and Dong {In Kim}}, journal = {IEEE Transactions on Vehicular Technology}, title = {Optimal Auction for Effective Energy Management in UAV-Assisted Vehicular Metaverse Synchronization Systems}, year = {2024}, volume = {73}, number = {1}, pages = {1207-1222}, doi = {10.1109/TVT.2023.3302411}, month = {Jan} }

Security Improvement for Deep Learning-Based Semantic Communication Systems

Tan, Nguyen, Huynh-The, Nguyen & Vo
2024 International Conference on Advanced Technologies for Communications (ATC)

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Semantic communication is becoming increasingly popular for wireless image transmission due to its superior communication efficiency. However, current deep learning-based semantic systems designed for semantic communication, though efficient, remain vulnerable to eavesdropping and often overlook security measures at the physical layer. To address this issue, this paper presents a deep learning-based system with joint source-channel coding (JSCC) and cyclical consistent generative adversarial network that enhances the security of semantic communication systems. We also design a convolutional neural network for the encoder and decoder which is trained to extract and transmit semantic features while minimizing the risk of privacy leakage. The artificial noise at the physical layer is employed at the source to degrade the eavesdropping ability of the eavesdropper. We show through experiments that under the artificial noise strategy, the legitimate user achieves a higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) than the eavesdropper. Moreover, the semantic system with JSCC offers better SSIM and PSNR than the separated source and channel coding models while preserving the confidentiality of semantic information during wireless transmission. This enhanced security framework opens new opportunities for secure and reliable communication of semantic information in diverse applications.

@inproceedings{10908341, author = {Hung Ngo Luu Tan and Van-Dinh Nguyen and Thien Huynh-The and Toan-Van Nguyen and Phuong Luu Vo}, booktitle = {2024 International Conference on Advanced Technologies for Communications (ATC)}, title = {Security Improvement for Deep Learning-Based Semantic Communication Systems}, year = {2024}, pages = {717-721}, doi = {10.1109/ATC63255.2024.10908341}, month = {Oct} }

Toward Privacy-Preserving Waste Classification in the Internet of Things

Quan, Nguyen, Nguyen, Wijayasundara, Setunge & Pathirana
IEEE Internet of Things Journal

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Diffuse waste data and associated privacy concerns present significant challenges for effective waste classification in the Internet of Things (IoT) realm. This research introduces a novel approach that leverages differential privacy (DP) and federated transfer learning (FTL) to address the issues, enabling waste classification while preserving privacy within the IoT ecosystem. By integrating federated learning (FL), transfer learning (TL), and DP, our proposed method facilitates collaborative training while ensuring data privacy. In this methodology, a pretrained model, initially trained on the ImageNet data set, is disseminated to IoT devices. Subsequently, these devices perform local training using the TrashNet and Garbage Classification data sets. This process allows devices to capture waste characteristics unique to their individual environments. Through the fusion of general knowledge pertaining to the trained model and local insights, the proposed approach achieves efficient waste classification. The study critically examines the implications for privacy, biases resulting from limited local data, and tradeoffs between privacy and model performance. The experimental evaluation demonstrates the effectiveness of the approach and underscores the importance of ensuring privacy-sensitive waste classification. This research contributes to the discourse on FTL and encourages further research into privacy-preserving waste classification within the IoT.

@article{10498074, author = {Minh K. Quan and Dinh C. Nguyen and Van-Dinh Nguyen and Mayuri Wijayasundara and Sujeeva Setunge and Pubudu N. Pathirana}, journal = {IEEE Internet of Things Journal}, title = {Toward Privacy-Preserving Waste Classification in the Internet of Things}, year = {2024}, volume = {11}, number = {14}, pages = {24814-24830}, doi = {10.1109/JIOT.2024.3386727}, month = {July} }

WiLHPE: WiFi-enabled Lightweight Channel Frequency Dynamic Convolution for HPE Tasks

Gian, Nguyen, Nguyen & Nguyen
2024 Tenth International Conference on Communications and Electronics (ICCE)

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Recently, there has been significant attention to WiFi-based human pose estimation (HPE) within the research community due to its device-free nature, cost-effectiveness, and privacy preservation. The implementation of such a solution requires improved model performance while upholding efficiency, particularly when employing resource-constrained devices. To address these challenges, this paper introduces a novel approach, the so-called WiLHPE, which integrates multi-modal sensors such as cameras and WiFi to accurately detect human pose landmarks. WiLHPE involves processing the raw WiFi signal through a novel neural network architecture to dynamically learn convolutional kernels weighted with attention across channel and frequency kernel spaces. This innovative approach diversifies the kernels to enhance the recognition capabilities of WiFi signals without introducing additional complexity, thus guaranteeing efficiency. Results conducted on the MM-Fi dataset underscore the superiority of WiLHPE over state-of-the-art approaches, all while ensuring minimal computational overhead. This makes the proposed approach highly suitable for large-scale scenarios.

@inproceedings{10634628, author = {Toan D. Gian and Tien-Hoa Nguyen and Nhan Thanh Nguyen and Van-Dinh Nguyen}, booktitle = {2024 Tenth International Conference on Communications and Electronics (ICCE)}, title = {WiLHPE: WiFi-enabled Lightweight Channel Frequency Dynamic Convolution for HPE Tasks}, year = {2024}, pages = {516-521}, doi = {10.1109/ICCE62051.2024.10634628}, month = {July} }

2023

Adaptive Service Placement, Task Offloading and Bandwidth Allocation in Task-Oriented URLLC Edge Networks

Van Huynh, Nguyen, Dobre, Khosravirad & Duong
ICC 2023 - IEEE International Conference on Communications

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Recently, the advances of low-latency communication technologies and edge intelligence have enabled a wide range of task-oriented time-sensitive applications. This paper aims at designing adaptive service placement, task offloading, and bandwidth allocation for ultra-reliable and low-latency communication (URLLC)-aided edge networks. The main objective is to minimise both the total end-to-end (e2e) latency and number of installed services at edge servers. The optimal solutions are obtained by jointly optimising service placement decisions, task offloading portions and bandwidth allocation at dynamic timescales subject to network budgets and application requirements under uncertain environment. Selective simulation results are provided to validate the effectiveness of the proposed solution in term of reducing the latency as well as optimising service placement decisions.

@inproceedings{10279120, author = {Dang {Van Huynh} and Van-Dinh Nguyen and Octavia A. Dobre and Saeed R. Khosravirad and Trung Q. Duong}, booktitle = {ICC 2023 - IEEE International Conference on Communications}, title = {Adaptive Service Placement, Task Offloading and Bandwidth Allocation in Task-Oriented URLLC Edge Networks}, year = {2023}, pages = {5755-5760}, doi = {10.1109/ICC45041.2023.10279120}, month = {May} }

Asymptotic Analysis of Max-Min Weighted SINR for IRS-Assisted MISO Systems With Hardware Impairments

Papazafeiropoulos, Pan, Elbir, Nguyen, Kourtessis & Chatzinotas
IEEE Wireless Communications Letters

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We focus on the realistic maximization of the uplink minimum signal-to-interference-plus-noise ratio (SINR) of a general multiple-input single-output (MISO) system assisted by an intelligent reflecting surface (IRS) in the large system limit accounting for HIs hardware impairments (HIs). In particular, we introduce the HIs at both the IRS (IRS-HIs) and the additive transceiver HIs (AT-HIs), usually neglected despite their inevitable impact. Specifically, the deterministic equivalent analysis enables the derivation of the asymptotic weighted maximum-minimum SINR with HIs by jointly optimizing the HIs-aware receiver, the transmit power, and the reflecting beamforming matrix (RBM). Notably, we obtain the optimal power allocation and reflecting beamforming matrix with low overhead instead of their frequent necessary computation in IRS-assisted MIMO systems based on the instantaneous channel information. Monte Carlo simulations verify the analytical results which show the insightful interplay among the key parameters and the degradation of the performance due to HIs.

@article{9477418, author = {Anastasios Papazafeiropoulos and Cunhua Pan and Ahmet M. Elbir and Van-Dinh Nguyen and Pandelis Kourtessis and Symeon Chatzinotas}, journal = {IEEE Wireless Communications Letters}, title = {Asymptotic Analysis of Max-Min Weighted SINR for IRS-Assisted MISO Systems With Hardware Impairments}, year = {2023}, volume = {12}, number = {2}, pages = {192-196}, doi = {10.1109/LWC.2021.3095678}, month = {Feb} }

Distributed Communication and Computation Resource Management for Digital Twin-Aided Edge Computing With Short-Packet Communications

Van Huynh, Nguyen, Khosravirad, Karagiannidis & Duong
IEEE Journal on Selected Areas in Communications

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For future networks, it is highly demanding to satisfy a wide range of time-sensitive and computation-intensive services. This is a very challenging task, since it requires a combination of aspects from information, communication and computation in order to establish a digital representation of the real network environment. This paper introduces a fairness-aware latency minimisation (FALM) framework in the digital twin (DT) aided edge computing with ultra-reliable and low latency communications (URLLC), which jointly optimises various communication and computation parameters, namely, bandwidth allocation, transmission power, task offloading portions, and processing rate of user equipments (UEs) and edge servers (ESs). The formulated problem is highly complicated, due to non-convex constraints and strong coupling among optimisation variables. To deal with this problem, we develop both centralised and distributed optimisation approaches. In particular, we first resort to successive convex approximation (SCA) method to develop a low-complexity iterative algorithm and solve the problem in a centralised manner. Combining tools from SCA and alternating direction method of multipliers (ADMM), we develop an efficient distributed solution with parallel computation processing at ESs under global consensus in each iteration and strong theoretical performance guaranteed. Numerical results are provided to validate the proposed solutions in terms of convergence speed and overall latency as well as improving fairness among all UEs.

@article{10234534, author = {Dang {Van Huynh} and Van-Dinh Nguyen and Saeed R. Khosravirad and George K. Karagiannidis and Trung Q. Duong}, journal = {IEEE Journal on Selected Areas in Communications}, title = {Distributed Communication and Computation Resource Management for Digital Twin-Aided Edge Computing With Short-Packet Communications}, year = {2023}, volume = {41}, number = {10}, pages = {3008-3021}, doi = {10.1109/JSAC.2023.3310087}, month = {Oct} }

Enabling Intelligent Traffic Steering in A Hierarchical Open Radio Access Network

Nguyen, Vu, Nguyen, Nguyen, Juntti, Luong, Hoang, Nguyen & Chatzinotas
GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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In this paper, we aim to enable an intelligent traffic (TS) steering application in the open radio access network (O-RAN) by jointly optimizing the flow-split distribution, congestion control and scheduling (i.e. so-called JFCS). To do so, we develop a multi-layer optimization framework based on network utility maximization and stochastic optimization methods. The proposed algorithm provides fast convergence, long-term utility-optimality and significantly low latency compared to state-of-the-art RAN approaches. In particular, our main contributions are as follows: i) we propose the novel JFCS framework to efficiently and adaptively route traffic to indented users in appropriate radio units, and ii) we develop low-complexity algorithms to effectively solve the JFCS problem in different time scales, enabling a closed-loop control of the TS in the O-RAN context. The insights presented in this work will pave the way for 0- RAN that are completely automated, offering improved control and flexibility.

@inproceedings{10437249, author = {Van-Dinh Nguyen and Thang X. Vu and Nhan Thanh Nguyen and Dinh C. Nguyen and Markku Juntti and Nguyen Cong Luong and Dinh Thai Hoang and Diep N. Nguyen and Symeon Chatzinotas}, booktitle = {GLOBECOM 2023 - 2023 IEEE Global Communications Conference}, title = {Enabling Intelligent Traffic Steering in A Hierarchical Open Radio Access Network}, year = {2023}, pages = {5232-5237}, doi = {10.1109/GLOBECOM54140.2023.10437249}, month = {Dec} }

Evolutionary Games for Dynamic Network Resource Selection in RSMA-Enabled 6G Networks

Thanh Van, Luong, Feng, Nguyen & Kim
IEEE Journal on Selected Areas in Communications

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In this paper, we address a dynamic network resource selection problem for mobile users in a rate-splitting multiple access (RSMA)-enabled network by leveraging evolutionary games. Particularly, mobile users are able to locally and dynamically make their selection on orthogonal resource blocks (RBs), which are also considered as network resources (NRs), over time to achieve their desired utilities. Then, RSMA is used for each group of users selecting the same NR. With the use of RSMA, the main goal is to optimize the beamformers of the common and private messages for users in the same group to maximize their sum rate. The resulting problem is generally non-convex, and thus we develop a successive convex approximation (SCA)-based algorithm to efficiently solve it in an iterative fashion. To model the NR adaptation of users, we propose to use two evolutionary games, i.e. a traditional evolutionary game (TEG) and fractional evolutionary game (FEG). The FEG approach enables users to incorporate memory effects (i.e. their past experiences) for their decision-making, which is more realistic than the TEG approach. We then theoretically verify the existence of the equilibrium of the proposed game approaches. Simulation results are provided to validate their consistency with the theoretical analysis and merits of the proposed approaches. They also reveal that, compared with TEG, FEG enables users to leverage past information for their decision-making, resulting in less communication overhead, while still guaranteeing convergence.

@article{10032158, author = {Nguyen Thi {Thanh Van} and Nguyen Cong Luong and Shaohan Feng and Van-Dinh Nguyen and Dong In Kim}, journal = {IEEE Journal on Selected Areas in Communications}, title = {Evolutionary Games for Dynamic Network Resource Selection in RSMA-Enabled 6G Networks}, year = {2023}, volume = {41}, number = {5}, pages = {1320-1335}, doi = {10.1109/JSAC.2023.3240779}, month = {May} }

Fairness-Aware Dynamic VNF Mapping and Scheduling in SDN/NFV-Enabled Satellite Edge Networks

Abreha, Chougrani, Maity, Nguyen, Chatzinotas & Politis
ICC 2023 - IEEE International Conference on Communications

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Satellite edge computing (SEC) has emerged as a promising technology to deliver network services to remote users. Coupled with software-defined networking (SDN) and network function virtualization (NFV), SEC can provide flexibility, agility, and efficiency when allocating computing and storage resources. However, there still remain a number of technical challenges in terms of fairness and efficiency of the allocation of physical resources in service provisioning, especially in a satellite network with limited resources and dynamic traffic demands. In this paper, we investigate a dynamic virtual network function (VNF) mapping and scheduling in an SDN/NFV-enabled SEC environment to maximize the fairness between competing services in terms of the E2E delay safe margin to enhance the service acceptance rates in the network. We mathematically formulate the VNF mapping and scheduling problem as a nonlinear integer optimization problem, which is NP-hard. In order to effectively solve the problem, this paper proposes a two-stage heuristic dynamic VNF mapping and scheduling algorithm: i) the path selection algorithm returns all possible paths for a given service request with multiple VNFs, which are sorted in ascending order based on their E2E service delay and executed offline, and ii) the dynamic VNF mapping and scheduling algorithm performs online dynamic remapping and rescheduling of VNFs. Finally, numerical results are provided to demonstrate that the proposed algorithm offers a higher service acceptance rate, computing resource utilization efficiency, and higher fairness compared to a benchmark scheme.

@inproceedings{10279545, author = {Haftay Gebreslasie Abreha and Houcine Chougrani and Ilora Maity and Van-Dinh Nguyen and Symeon Chatzinotas and Christos Politis}, booktitle = {ICC 2023 - IEEE International Conference on Communications}, title = {Fairness-Aware Dynamic VNF Mapping and Scheduling in SDN/NFV-Enabled Satellite Edge Networks}, year = {2023}, pages = {4892-4898}, doi = {10.1109/ICC45041.2023.10279545}, month = {May} }

Intelligent Traffic Steering in Beyond 5G Open RAN Based on LSTM Traffic Prediction

Kavehmadavani, Nguyen, Vu & Chatzinotas
IEEE Transactions on Wireless Communications

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Open radio access network (ORAN) Alliance offers a disaggregated RAN functionality built using open interface specifications between blocks. To efficiently support various competing services, namely enhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC), the ORAN Alliance has introduced a standard approach toward more virtualized, open, and intelligent networks. To realize the benefits of ORAN in optimizing resource utilization, this paper studies an intelligent traffic steering (TS) scheme within the proposed disaggregated ORAN architecture. For this purpose, we propose a joint intelligent traffic prediction, flow-split distribution, dynamic user association, and radio resource management (JIFDR) framework in the presence of unknown dynamic traffic demands. To adapt to dynamic environments on different time scales, we decompose the formulated optimization problem into two long-term and short-term subproblems, where the optimality of the latter is strongly dependent on the optimal dynamic traffic demand. We then apply a long-short-term memory (LSTM) model to effectively solve the long-term subproblem, aiming to predict dynamic traffic demands, RAN slicing, and flow-split decisions. The resulting non-convex short-term subproblem is converted to a more computationally tractable form by exploiting successive convex approximations. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms compared to several well-known benchmark schemes.

@article{10071958, author = {Fatemeh Kavehmadavani and Van-Dinh Nguyen and Thang X. Vu and Symeon Chatzinotas}, journal = {IEEE Transactions on Wireless Communications}, title = {Intelligent Traffic Steering in Beyond 5G Open RAN Based on LSTM Traffic Prediction}, year = {2023}, volume = {22}, number = {11}, pages = {7727-7742}, doi = {10.1109/TWC.2023.3254903}, month = {Nov} }

Joint Communication and Computation Offloading for Ultra-Reliable and Low-Latency With Multi-Tier Computing

Van Huynh, Nguyen, Chatzinotas, Khosravirad, Poor & Duong
IEEE Journal on Selected Areas in Communications

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In this paper, we study joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. The formulated JCCO problem belongs to a difficult class of mixed-integer non-convex optimization problem, making it computationally intractable. In addition, a strong coupling between binary and continuous variables and the large size of hierarchical edge-cloud systems make the problem even more challenging to solve optimally. To address these challenges, we first decompose the original problem into two subproblems based on the unique structure of the underlying problem and leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. To speed up optimal user association searching, we incorporate a penalty function into the objective function to resolve uncertainties of a binary nature. Two sub-optimal designs for given user association policies based on channel conditions and random user associations are also investigated to serve as state-of-the-art benchmarks. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

@article{9978295, author = {Dang {Van Huynh} and Van-Dinh Nguyen and Symeon Chatzinotas and Saeed R. Khosravirad and H. Vincent Poor and Trung Q. Duong}, journal = {IEEE Journal on Selected Areas in Communications}, title = {Joint Communication and Computation Offloading for Ultra-Reliable and Low-Latency With Multi-Tier Computing}, year = {2023}, volume = {41}, number = {2}, pages = {521-537}, doi = {10.1109/JSAC.2022.3227088}, month = {Feb} }

Joint Communications and Sensing Design for Multi-Carrier MIMO Systems

Nguyen, Shlezinger, Ngo, Nguyen & Juntti
2023 IEEE Statistical Signal Processing Workshop (SSP)

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In conventional joint communications and sensing (JCAS) designs for multi-carrier multiple-input multiple-output (MIMO) systems, the dual-functional waveforms are often optimized for the whole frequency band, resulting in limited communications–sensing performance tradeoff. To overcome the limitation, we propose employing a subset of subcarriers for JCAS, while the communications function is performed over all the subcarriers. This offers more degrees of freedom to enhance the communications performance under a given sensing accuracy. We first formulate the rate maximization under the sensing accuracy constraint to optimize the beamformers and JCAS subcarriers. The problem is solved via Riemannian manifold optimization and closed-form solutions. Numerical results for an 8 × 4 MIMO system with 64 subcarriers show that compared to the conventional subcarrier sharing scheme, the proposed scheme employing 16 JCAS subcarriers offers 60% improvement in the achievable communications rate at the signal-to-noise ratio of 10 dB. Meanwhile, this scheme generates the sensing beampattern with the same quality as the conventional JCAS design.

@inproceedings{10207952, author = {Nhan Thanh Nguyen and Nir Shlezinger and Khac-Hoang Ngo and Van-Dinh Nguyen and Markku Juntti}, booktitle = {2023 IEEE Statistical Signal Processing Workshop (SSP)}, title = {Joint Communications and Sensing Design for Multi-Carrier MIMO Systems}, year = {2023}, pages = {110-114}, doi = {10.1109/SSP53291.2023.10207952}, month = {July} }

Joint Resource Allocation and Link Adaptation for Ultra-Reliable and Low-Latency Services

Hossen, Vu, Nguyen, Chatzinotas & Ottersten
2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)

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With the emergence of ultra-reliable and low latency communication (URLLC) services, link adaptation (LA) plays a pivotal role in improving the robustness and reliability of communication networks via appropriate modulation and coding schemes (MCS). LA-based resource management schemes in both physical and medium access control layers can significantly enhance the system performance in terms of throughput, latency, reliability, and quality of service. Increasing the number of retransmissions will achieve higher reliability and increase transmission latency. In order to balance this trade-off with improved link performance for URLLC services, we study a joint subcarrier and power allocation problem to maximize the achievable sum-rate under an appropriate MCS. The formulated problem is mixed-integer nonconvex programming which is challenging to solve optimally. In addition, a direct application of standard optimization techniques is no longer applicable due to the complication of the effective signal-to-noise ratio (SNR) function. To overcome this challenge, we first relax the binary variables to continuous ones and introduce additional variables to convert the relaxed problem into a more tractable form. By leveraging the successive convex approximation method, we develop a low-complexity iterative algorithm that guarantees to achieve at least a locally optimal solution. Simulation results are provided to show the fast convergence of the proposed iterative algorithm and demonstrate the significant performance improvement in terms of the achievable sum-rate, compared with the conventional LA approach and existing retransmission policy.

@inproceedings{10060392, author = {Md Arman Hossen and Thang X. Vu and Van-Dinh Nguyen and Symeon Chatzinotas and Bj{\"o}rn Ottersten}, booktitle = {2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)}, title = {Joint Resource Allocation and Link Adaptation for Ultra-Reliable and Low-Latency Services}, year = {2023}, pages = {757-762}, doi = {10.1109/CCNC51644.2023.10060392}, month = {Jan} }

Optimal Auction for Effective Energy Management for UAV-assisted Metaverse Synchronization System

Luong, Chau, Duy Anh, Sang, Feng, Nguyen, Niyato & Kim
2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)

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In this paper, we investigate an effective energy management in a UAV -assisted Metaverse synchronization system. The UAV s perform the data collection for a virtual service provider (VSP) for the synchronization between the physical objects and digital twins (DTs). The UAVs buy energy resources from an energy service provider (ESP). The key issue is to motivate both the ESP and the UAV s to participate in the energy trading market. For this, we design a deep learning (DL)-based auction scheme that maximizes the revenue of the ESP while guaranteeing individual rationality (IR) and incentive compatibility (IC). We provide numerical results to demonstrate the improvement of the DL-based auction scheme compared to the baseline scheme in terms of revenue, IC, and IR.

@inproceedings{10059864, author = {Nguyen Cong Luong and Le Khac Chau and Nguyen Do {Duy Anh} and Nguyen Huu Sang and Shaohan Feng and Van-Dinh Nguyen and Dusit Niyato and Dong In Kim}, booktitle = {2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)}, title = {Optimal Auction for Effective Energy Management for UAV-assisted Metaverse Synchronization System}, year = {2023}, pages = {392-397}, doi = {10.1109/CCNC51644.2023.10059864}, month = {Jan} }

RIS-Assisted Full-Duplex Integrated Sensing and Communication

Nhat Le, Nguyen, Dobre & Shin
IEEE Wireless Communications Letters

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In this letter, we explore the application of reconfigurable intelligent surface (RIS) in the integrated sensing and communication network, where a full-duplex multi-antenna base station (BS) concurrently detects a target and communicates with a user equipment (UE). Our objective is to maximize the UE’s transmission rate by jointly optimizing the BS’s transmit beamforming, UE’s transmit power, and RIS’s phase shifts, while satisfying the condition on the minimum required sensing power. We develop a block coordinate ascend-based iterative algorithm to solve the formulated problem, which guarantees the convergence to at least a local optimum. Numerical results show the efficiency of the proposed solution as well as the trade-off between the UE’s transmission rate and the required sensing power, along with the efficiency of employing RIS.

@article{10149099, author = {Quang {Nhat Le} and Van-Dinh Nguyen and Octavia A. Dobre and Hyundong Shin}, journal = {IEEE Wireless Communications Letters}, title = {RIS-Assisted Full-Duplex Integrated Sensing and Communication}, year = {2023}, volume = {12}, number = {10}, pages = {1677-1681}, doi = {10.1109/LWC.2023.3285391}, month = {Oct} }

Short-Packet Communications in Multihop Networks With WET: Performance Analysis and Deep Learning-Aided Optimization

Nguyen, Nguyen, da Costa, Huynh-The, Hu & An
IEEE Transactions on Wireless Communications

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In this paper, we study short-packet communications in multi-hop networks with wireless energy transfer, where relay nodes harvest energy from power beacons to transmit short packets to multiple destinations. It is proposed a novel cooperative beamforming relay selection (CRS) scheme which incorporates partial relay selection and distributed multiuser beamforming to achieve a high-reliable transmission in two consecutive hops. A closed-form expression for the average block error rate (BLER) of the CRS scheme is derived, based on which an asymptotic analysis is also carried out. To achieve optimal channel uses allocation, we formulate a fairness end-to-end throughput maximization problem which is generally NP-hard due to the non-concavity of the objective function and mixed-integer constraints. To solve this challenging problem efficiently, we first relax channel uses to be continuous and transform the relaxed problem into an equivalent non-convex one, but with a more tractable form. We then develop a low-complexity iterative algorithm relying on inner approximation framework to convexify non-convex parts that converges to at least a locally optimal solution. Towards real-time settings, we design an efficient deep convolutional neural network (CNN) with multiscale-accumulation connections to achieve the sub-optimal solution of the relaxed problem via real-time inference processes. Numerical results are presented to verify the analytical derivations and to demonstrate performance improvements of the CRS scheme over the benchmark ones in terms of BLER, reliability, latency, and throughput in various settings. Moreover, the designed CNN provides the lowest root-mean-square error compared to the state-of-the-art deep learning approaches while the CNN-aided optimization framework estimates accurately the optimal channel uses allocation with low execution time.

@article{9852154, author = {Toan-Van Nguyen and Van-Dinh Nguyen and Daniel Benevides {da Costa} and Thien Huynh-The and Rose Qingyang Hu and Beongku An}, journal = {IEEE Transactions on Wireless Communications}, title = {Short-Packet Communications in Multihop Networks With WET: Performance Analysis and Deep Learning-Aided Optimization}, year = {2023}, volume = {22}, number = {1}, pages = {439-456}, doi = {10.1109/TWC.2022.3195234}, month = {Jan} }

Spectral Efficiency Analysis of Hybrid Relay-Reflecting Intelligent Surface-Assisted Cell-Free Massive MIMO Systems

Nguyen, Nguyen, Nguyen, Ngo, Chatzinotas & Juntti
IEEE Transactions on Wireless Communications

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A cell-free (CF) massive multiple-input-multiple-output (mMIMO) system can provide uniform spectral efficiency (SE) with simple signal processing. On the other hand, a recently introduced technology called hybrid relay-reflecting intelligent surface (HR-RIS) can customize the physical propagation environment by simultaneously reflecting and amplifying radio waves in preferred directions. Thus, it is natural that incorporating HR-RIS into CF mMIMO can be a symbiotic convergence of these two technologies for future wireless communications. This motivates us to consider an HR-RIS-aided CF mMIMO system to utilize their combined benefits. We first model the uplink/downlink channels and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis of SE performance of the considered system. Specifically, we derive closed-form expressions for the uplink and downlink SE. The results reveal important observations on the performance gains achieved by HR-RISs compared to conventional systems. The presented analytical results are also valid for conventional CF mMIMO systems and those aided by passive reconfigurable intelligent surfaces. Such results play an important role in designing new transmission strategies and optimizing HR-RIS-aided CF mMIMO systems. Finally, we provide extensive numerical results to verify the analytical derivations and the effectiveness of the proposed system design under various settings.

@article{9940169, author = {Nhan Thanh Nguyen and Van-Dinh Nguyen and Hieu Van Nguyen and Hien Quoc Ngo and Symeon Chatzinotas and Markku Juntti}, journal = {IEEE Transactions on Wireless Communications}, title = {Spectral Efficiency Analysis of Hybrid Relay-Reflecting Intelligent Surface-Assisted Cell-Free Massive MIMO Systems}, year = {2023}, volume = {22}, number = {5}, pages = {3397-3416}, doi = {10.1109/TWC.2022.3217828}, month = {May} }

2022

An Efficient Deep CNN Design for EH Short-Packet Communications in Multihop Cognitive IoT Networks

Nguyen, Huynh-The, Nguyen, Da Costa, Qingyang Hu & An
ICC 2022 - IEEE International Conference on Communications

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In this paper, we design an efficient deep convolutional neural network (CNN) to improve and predict the performance of energy harvesting (EH) short-packet communications in multi-hop cognitive Internet-of-Things (IoT) networks. Specifically, we propose a Sum-EH scheme that allows IoT nodes to harvest energy from either a power beacon or primary transmitters to improve not only packet transmissions but also energy harvesting capabilities. We then build a novel deep CNN framework with feature enhancement-collection blocks based on the proposed Sum-EH scheme to simultaneously estimate the block error rate (BLER) and throughput with high accuracy and low execution time. Simulation results show that the proposed CNN framework achieves almost exactly the BLER and throughput of Sum-EH one, while it considerably reduces computational complexity, suggesting a real-time setting for IoT systems under complex scenarios. Moreover, the designed CNN model achieves the root-mean-square-error (RMSE) of 1.33 × 10-2 on the considered dataset, which exhibits the lowest RMSE compared to the deep neural network and state-of-the-art machine learning approaches.

@inproceedings{9839014, author = {Toan-Van Nguyen and Thien Huynh-The and Van-Dinh Nguyen and Daniel Benevides {Da Costa} and Rose {Qingyang Hu} and Beongku An}, booktitle = {ICC 2022 - IEEE International Conference on Communications}, title = {An Efficient Deep CNN Design for EH Short-Packet Communications in Multihop Cognitive IoT Networks}, year = {2022}, pages = {2102-2107}, doi = {10.1109/ICC45855.2022.9839014}, month = {May} }

Digital Twin Empowered Ultra-Reliable and Low-Latency Communications-based Edge Networks in Industrial IoT Environment

Van Huynh, Nguyen, Sharma, Dobre & Duong
ICC 2022 - IEEE International Conference on Communications

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We address the problem of minimising latency with computation offloading in digital twin wireless edge networks in industrial Internet-of-Things environment via ultra-reliable and low latency communications links. The minimised latency is obtained by jointly optimising both communication and computation variables, namely transmit power, user association of IoT devices, offloading portions, the processing rate of users and edge servers. To deal with this challenging problem, we propose an iterative algorithm based on alternating optimisation approach combined with inner convex approximation framework. Simulation results demonstrate the proposed algorithm’s effectiveness in reducing the latency compared with other benchmark schemes.

@inproceedings{9838860, author = {Dang {Van Huynh} and Van-Dinh Nguyen and Vishal Sharma and Octavia A. Dobre and Trung Q. Duong}, booktitle = {ICC 2022 - IEEE International Conference on Communications}, title = {Digital Twin Empowered Ultra-Reliable and Low-Latency Communications-based Edge Networks in Industrial IoT Environment}, year = {2022}, pages = {5651-5656}, doi = {10.1109/ICC45855.2022.9838860}, month = {May} }

Downlink Throughput of Cell-Free Massive MIMO Systems Assisted by Hybrid Relay-Reflecting Intelligent Surfaces

Nguyen, Nguyen, Nguyen, Ngo, Chatzinotas & Juntti
ICC 2022 - IEEE International Conference on Communications

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We consider in this work a cell-free (CF) massive multiple-input-multiple-output (mMIMO) system where multiple hybrid relay-reflecting intelligent surfaces (HR-RIS) are deployed to assist communication between access points and users. We first present the signal model and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis for the considered HR-RIS-aided CF mMIMO system, where the closed-form expression of the downlink throughput is derived. The presented analytical results are also valid for conventional CF mMIMO systems, i.e., CF mMIMO systems with and without passive reconfigurable intelligent surfaces. Finally, the analytical derivations are verified by extensive numerical results.

@inproceedings{9838672, author = {Nhan T. Nguyen and V.-Dinh Nguyen and Hieu V. Nguyen and Hien Q. Ngo and Symeon Chatzinotas and Markku Juntti}, booktitle = {ICC 2022 - IEEE International Conference on Communications}, title = {Downlink Throughput of Cell-Free Massive MIMO Systems Assisted by Hybrid Relay-Reflecting Intelligent Surfaces}, year = {2022}, pages = {1475-1480}, doi = {10.1109/ICC45855.2022.9838672}, month = {May} }

Fairness-aware Latency Minimisation in Digital Twin-aided Edge Computing with Ultra-Reliable and Low-Latency Communications: A Distributed Optimisation Approach (Invited Paper)

Van Huynh, Nguyen, Khosravirad & Duong
2022 56th Asilomar Conference on Signals, Systems, and Computers

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The advanced development of communication technologies and computing platforms open opportunities to enable a wide range of time-sensitive services. However, designing an effective optimisation solution to deal with joint communication and computation resources is a challenging research direction. This paper addresses a fairness-aware latency minimisation problem in the digital twin (DT) aided edge computing with ultrareliable and low latency communications (URLLC). The optimal solution is obtained by jointly optimising various variables, namely, bandwidth allocation, transmit power, task offloading policies, and the processing rate of user equipment (UE) and edge server (ES). The formulated optimisation problem is highly complicated with many non-convex constraints and strong coupling variables. To deal with the problem, we propose a distributed optimisation solution based on the global consensus approach and the successive convex approximation framework (SCA). Selected numerical results are provided to validate the proposed solution in terms of minimise latency as well as improved fairness among all UEs in the DT network.

@inproceedings{10051857, author = {Dang {Van Huynh} and Van-Dinh Nguyen and Saeed R. Khosravirad and Trung Q. Duong}, booktitle = {2022 56th Asilomar Conference on Signals, Systems, and Computers}, title = {Fairness-aware Latency Minimisation in Digital Twin-aided Edge Computing with Ultra-Reliable and Low-Latency Communications: A Distributed Optimisation Approach (Invited Paper)}, year = {2022}, pages = {1045-1049}, doi = {10.1109/IEEECONF56349.2022.10051857}, month = {Oct} }

FedFog: Network-Aware Optimization of Federated Learning Over Wireless Fog-Cloud Systems

Nguyen, Chatzinotas, Ottersten & Duong
IEEE Transactions on Wireless Communications

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Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fog-cloud system (e.g., non-i.i.d. data, users’ heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called $\mathsf {FedFog}$ ) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud. Next, we employ $\mathsf {FedFog}$ in wireless fog-cloud systems by investigating a novel network-aware FL optimization problem that strikes the balance between the global loss and completion time. An iterative algorithm is then developed to obtain a precise measurement of the system performance, which helps design an efficient stopping criteria to output an appropriate number of global rounds. To mitigate the straggler effect, we propose a flexible user aggregation strategy that trains fast users first to obtain a certain level of accuracy before allowing slow users to join the global training updates. Extensive numerical results using several real-world FL tasks are provided to verify the theoretical convergence of $\mathsf {FedFog}$ . We also show that the proposed co-design of FL and communication is essential to substantially improve resource utilization while achieving comparable accuracy of the learning model.

@article{9760729, author = {Van-Dinh Nguyen and Symeon Chatzinotas and Bj{\"o}rn Ottersten and Trung Q. Duong}, journal = {IEEE Transactions on Wireless Communications}, title = {FedFog: Network-Aware Optimization of Federated Learning Over Wireless Fog-Cloud Systems}, year = {2022}, volume = {21}, number = {10}, pages = {8581-8599}, doi = {10.1109/TWC.2022.3167263}, month = {Oct} }

Hybrid Active-Passive Reconfigurable Intelligent Surface-Assisted Multi-User MISO Systems

Nguyen, Nguyen, Wu, Tölli, Chatzinotas & Juntti
2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

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We consider a multi-user multiple-input single-output (MISO) communications system which is assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). Unlike conventional passive RISs, hybrid RIS is equipped with a few active elements with the ability to reflect and amplify incident signals to significantly improve the system performance. Towards a fairness-oriented design, we maximize the minimum rate among all users through jointly optimizing the transmit beamforming vectors and RIS reflecting/amplifying coefficients. Combining tools from block coordinate ascent and successive convex approximation, the challenging nonconvex problem is efficiently solved by a low-complexity iterative algorithm. The numerical results show that a hybrid RIS with 4 active elements out of a total of 50 elements with a power budget of −1 dBm offers an improvement of up to 80% to the considered system, while that achieved by a fully passive RIS is only 27%.

@inproceedings{9833956, author = {Nhan T. Nguyen and V.-Dinh Nguyen and Qingqing Wu and Antti T{\"o}lli and Symeon Chatzinotas and Markku Juntti}, booktitle = {2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)}, title = {Hybrid Active-Passive Reconfigurable Intelligent Surface-Assisted Multi-User MISO Systems}, year = {2022}, pages = {1-5}, doi = {10.1109/SPAWC51304.2022.9833956}, month = {July} }

Hybrid Active-Passive Reconfigurable Intelligent Surface-Assisted UAV Communications

Nguyen, Nguyen, Wu, Tölli, Chatzinotas & Juntti
GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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We consider a novel hybrid active-passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) air-ground communications system. Unlike the conventional passive RIS, the hybrid RIS is equipped with a few active elements to not only reflect but also amplify the incident signals for significant performance improvement. Towards a fairness design, our goal is to maximize the minimum rate among users through jointly optimizing the location and power allocation of the UAV and the RIS reflecting/amplifying coefficients. The formulated optimization problem is nonconvex and challenging, which is efficiently solved via block coordinate descend and successive convex approximation. Our numerical results show that a hybrid RIS requires only 4 active elements and a power budget of 0 dBm to achieve an improvement of 52.08% in the minimum rate, while that achieved by a conventional passive RIS with the same total number of elements is only 18.06%.

@inproceedings{10001719, author = {Nhan T. Nguyen and V.-Dinh Nguyen and Qingqing Wu and Antti T{\"o}lli and Symeon Chatzinotas and Markku Juntti}, booktitle = {GLOBECOM 2022 - 2022 IEEE Global Communications Conference}, title = {Hybrid Active-Passive Reconfigurable Intelligent Surface-Assisted UAV Communications}, year = {2022}, pages = {3126-3131}, doi = {10.1109/GLOBECOM48099.2022.10001719}, month = {Dec} }

Intelligent Blockchain-Based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges

Nguyen, Nguyen, Ding, Chatzinotas, Pathirana, Seneviratne, Dobre & Zomaya
IEEE Network

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The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things (IoT) networks, enabling task offloading with security enhancement based on blockchain mining. Yet the existing approaches for these enabling technologies are isolated, providing only tailored solutions for specific services and scenarios. To fill this gap, we propose a novel cooperative task offloading and blockchain mining (TOBM) scheme for a blockchain-based MEC system, where each edge device not only handles computation tasks but also conducts block mining for improving system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. To accommodate the highly dynamic environment and high-dimensional system state space, we apply a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. Experimental results demonstrate the superior performance of the proposed TOBM scheme in terms of enhanced system reward, improved offloading utility with lower blockchain mining latency, and better system utility, compared to the existing cooperative and non-cooperative schemes. The article concludes with key technical challenges and possible directions for future blockchain-based MEC research.

@article{9839636, author = {Dinh C. Nguyen and Van-Dinh Nguyen and Ming Ding and Symeon Chatzinotas and Pubudu N. Pathirana and Aruna Seneviratne and Octavia Dobre and Albert Y. Zomaya}, journal = {IEEE Network}, title = {Intelligent Blockchain-Based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges}, year = {2022}, volume = {36}, number = {6}, pages = {12-19}, doi = {10.1109/MNET.002.2100188}, month = {November} }

Maximizing the Number of Served Users in a Smart City using Reconfigurable Intelligent Surfaces

Zivuku, Kisseleff, Nguyen, Ntontin, Martins, Chatzinotas & Ottersten
2022 IEEE Wireless Communications and Networking Conference (WCNC)

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Among a plethora of new wireless communication technologies, reconfigurable intelligent surface (RIS) emerges as one of the revolutionary solutions to provide energy- and cost-efficient signal transmissions. RIS is capable of reflecting electromagnetic signals in a controlled manner. In this paper, we jointly design the active beamforming at the base station and passive beamforming at the RIS to maximize the number of served users in a practical Smart City street scenario, subject to quality of service (QoS) and power constraints. The formulated problem belongs to the difficult class of mixed-integer non-convex programming, which is NP-hard. To arrive at a low-complexity solution, we first decompose the original problem into two subproblems and then propose an alternating optimization algorithm based on successive convex approximation (SCA) to solve them in an iterative manner. Simulation results are provided to verify the performance improvement of the proposed algorithm as compared to baseline schemes.

@inproceedings{9771746, author = {Progress Zivuku and Steven Kisseleff and Van-Dinh Nguyen and Konstantinos Ntontin and Wallace A. Martins and Symeon Chatzinotas and Bj{\"o}rn Ottersten}, booktitle = {2022 IEEE Wireless Communications and Networking Conference (WCNC)}, title = {Maximizing the Number of Served Users in a Smart City using Reconfigurable Intelligent Surfaces}, year = {2022}, pages = {494-499}, doi = {10.1109/WCNC51071.2022.9771746}, month = {April} }

Minimising Offloading Latency for Edge-Cloud Systems with Ultra-Reliable and Low-Latency Communications

Van Huynh, Nguyen, Khosravirad & Duong
ICC 2022 - IEEE International Conference on Communications

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We study a joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the worst-case end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. To tackle the problem, we first decompose the original problem into two subproblems and then leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. The numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

@inproceedings{9839148, author = {Dang {Van Huynh} and Van-Dinh Nguyen and Saeed R. Khosravirad and Trung Q. Duong}, booktitle = {ICC 2022 - IEEE International Conference on Communications}, title = {Minimising Offloading Latency for Edge-Cloud Systems with Ultra-Reliable and Low-Latency Communications}, year = {2022}, pages = {5122-5127}, doi = {10.1109/ICC45855.2022.9839148}, month = {May} }

Task-Oriented Communication Design in Cyber-Physical Systems: A Survey on Theory and Applications

Mostaani, Vu, Sharma, Nguyen, Liao & Chatzinotas
IEEE Access

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Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate, but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm. In parallel, it is projected that by 2023, half of the worldwide network connections will be among machines rather than humans. In this context, it is crucial to establish a new paradigm for designing communication strategies for multi-agent cyber-physical systems. This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication designs. In this direction, this paper reviews and structures the relevant theoretical work across a wide range of scientific communities. Subsequently, it proposes a general conceptual framework for task-oriented communication design, along with its specializations according to targeted use cases. Furthermore, it provides a survey of relevant contributions in dominant applications, such as industrial internet of things, multi-unmanned aerial vehicle (UAV) systems, autonomous vehicles, distributed learning systems, smart manufacturing plants, 5G and beyond self-organizing networks, and tactile internet. Finally, this paper also highlights the most important open research topics from both the theoretical framework and application points of view.

@article{9994683, author = {Arsham Mostaani and Thang X. Vu and Shree Krishna Sharma and Van-Dinh Nguyen and Qi Liao and Symeon Chatzinotas}, journal = {IEEE Access}, title = {Task-Oriented Communication Design in Cyber-Physical Systems: A Survey on Theory and Applications}, year = {2022}, volume = {10}, pages = {133842-133868}, doi = {10.1109/ACCESS.2022.3231039} }

Traffic Steering for eMBB and uRLLC Coexistence in Open Radio Access Networks

Kavehmadavani, Nguyen, Vu & Chatzinotas
2022 IEEE International Conference on Communications Workshops (ICC Workshops)

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Existing radio access network (RAN) architectures are lack of sufficient openness, flexibility, and intelligence to meet the diverse demands of emerging services in beyond 5G and 6G wireless networks, including enhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC). Open RAN (ORAN) is a promising paradigm that allows building a virtualized and intelligent architecture. In this paper, we focus on traffic steering (TS) scheme based on multi-connectivity (MC) and network slicing (NS) techniques to efficiently allocate heterogeneous network resources in “NextG” cellular networks. We formulate the RAN resource allocation problem to simultaneously maximize the weighted sum eMBB throughput and minimize the worst-user uRLLC latency subject to QoS requirements, and orthogonality, power, and limited fronthaul constraints. Since the formulated problem is categorized as a mixed integer nonlinear problem (MINLP), we first relax binary variables to continuous ones and develop an efficient iterative algorithm based on successive convex approximation technique. System-level simulation results demon-strate the effectiveness of the proposed algorithm, compared to several well-known benchmark schemes.

@inproceedings{9814611, author = {Fatemeh Kavehmadavani and Van-Dinh Nguyen and Thang X. Vu and Symeon Chatzinotas}, booktitle = {2022 IEEE International Conference on Communications Workshops (ICC Workshops)}, title = {Traffic Steering for eMBB and uRLLC Coexistence in Open Radio Access Networks}, year = {2022}, pages = {242-247}, doi = {10.1109/ICCWorkshops53468.2022.9814611}, month = {May} }

UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization

Tran, Nguyen, Chatzinotas, Vu & Ottersten
IEEE Transactions on Wireless Communications

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Unmanned aerial vehicle (UAV) communication has emerged as a prominent technology for emergency communications (e.g., natural disaster) in the Internet of Things (IoT) networks to enhance the ability of disaster prediction, damage assessment, and rescue operations promptly. A UAV can be deployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway (GW). In general, the latency constraint at IoT devices and UAV’s limited storage capacity highly hinder practical applications of UAV-assisted IoT networks. In this paper, full-duplex (FD) radio is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes (viz., FD and HD). Herein, a device is considered to be successfully served if its data is collected by the UAV and conveyed to GW timely during flight time. In this context, we aim to maximize the number of served IoT devices by jointly optimizing bandwidth, power allocation, and the UAV trajectory while satisfying each device’s requirement and the UAV’s limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. Towards appealing applications, we first relax binary variables into continuous ones and transform the original problem into a more computationally tractable form. By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and system throughput.

@article{9522072, author = {Dinh-Hieu Tran and Van-Dinh Nguyen and Symeon Chatzinotas and Thang X. Vu and Bj{\"o}rn Ottersten}, journal = {IEEE Transactions on Wireless Communications}, title = {UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization}, year = {2022}, volume = {21}, number = {3}, pages = {1621-1637}, doi = {10.1109/TWC.2021.3105821}, month = {March} }

URLLC Edge Networks With Joint Optimal User Association, Task Offloading and Resource Allocation: A Digital Twin Approach

Van Huynh, Nguyen, Khosravirad, Sharma, Dobre, Shin & Duong
IEEE Transactions on Communications

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This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.

@article{9885226, author = {Dang {Van Huynh} and Van-Dinh Nguyen and Saeed R. Khosravirad and Vishal Sharma and Octavia A. Dobre and Hyundong Shin and Trung Q. Duong}, journal = {IEEE Transactions on Communications}, title = {URLLC Edge Networks With Joint Optimal User Association, Task Offloading and Resource Allocation: A Digital Twin Approach}, year = {2022}, volume = {70}, number = {11}, pages = {7669-7682}, doi = {10.1109/TCOMM.2022.3205692}, month = {Nov} }

2021

Efficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks

Nguyen, Sharma, Vu, Chatzinotas & Ottersten
IEEE Internet of Things Journal

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Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication overhead. However, FL still faces a number of challenges such as nonindependent and identically distributed data and heterogeneity of user equipments (UEs). Enabling a large number of UEs to join the training process in every round raises a potential issue of the heavy global communication burden. To address these issues, we generalize the current state-of-the-art federated averaging (FedAvg) by adding a weight-based proximal term to the local loss function. The proposed FL algorithm runs stochastic gradient descent in parallel on a sampled subset of the total UEs with replacement during each global round. We provide a convergence upper bound characterizing the tradeoff between convergence rate and global rounds, showing that a small number of active UEs per round still guarantees convergence. Next, we employ the proposed FL algorithm in wireless Internet-of-Things (IoT) networks to minimize either total energy consumption or completion time of FL, where a simple yet efficient path-following algorithm is developed for its solutions. Finally, numerical results on unbalanced data sets are provided to demonstrate the performance improvement and robustness on the convergence rate of the proposed FL algorithm over FedAvg. They also reveal that the proposed algorithm requires much less training time and energy consumption than the FL algorithm with full user participation. These observations advocate the proposed FL algorithm for a paradigm shift in bandwidth-constrained learning wireless IoT networks.

@article{9187874, author = {Van-Dinh Nguyen and Shree Krishna Sharma and Thang X. Vu and Symeon Chatzinotas and Bj{\"o}rn Ottersten}, journal = {IEEE Internet of Things Journal}, title = {Efficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks}, year = {2021}, volume = {8}, number = {5}, pages = {3394-3409}, doi = {10.1109/JIOT.2020.3022534}, month = {March} }

Energy Efficiency Maximization in RIS-Aided Cell-Free Network With Limited Backhaul

Le, Nguyen, Dobre & Zhao
IEEE Communications Letters

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Integrating the reconfigurable intelligent surface in a cell-free (RIS-CF) network is an effective solution to improve the capacity and coverage of future wireless systems with low cost and power consumption. The reflecting coefficients of RISs can be programmed to enhance signals received at users. This letter addresses a joint design of transmit beamformers at access points and reflecting coefficients at RISs to maximize the energy efficiency (EE) of RIS-CF networks, taking into account the limited backhaul capacity constraints. Due to a very computationally challenging nonconvex problem, we develop a simple yet efficient alternating algorithm for its solution. Numerical results verify that the EE of RIS-CF networks is greatly improved, showing the benefit of using RISs.

@article{9363171, author = {Quang Nhat Le and Van-Dinh Nguyen and Octavia A. Dobre and Ruiqin Zhao}, journal = {IEEE Communications Letters}, title = {Energy Efficiency Maximization in RIS-Aided Cell-Free Network With Limited Backhaul}, year = {2021}, volume = {25}, number = {6}, pages = {1974-1978}, doi = {10.1109/LCOMM.2021.3062275}, month = {June} }

Learning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks

Le, Nguyen, Dobre, Nguyen, Zhao & Chatzinotas
IEEE Transactions on Vehicular Technology

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The superior spectral efficiency (SE) and user fairness feature of non-orthogonal multiple access (NOMA) systems are achieved by exploiting user clustering (UC) more efficiently. However, a random UC certainly results in a suboptimal solution while an exhaustive search method comes at the cost of high complexity, especially for systems of medium-to-large size. To address this problem, we develop two efficient unsupervised machine learning based UC algorithms, namely k-means++ and improved k-means++, to effectively cluster users into disjoint clusters in cell-free massive multiple-input multiple-output (CFmMIMO) system. Adopting full-pilot zero-forcing at access points (APs) to comprehensively assess the system performance, we formulate the sum SE optimization problem taking into account power constraints at APs, necessary conditions for implementing successive interference cancellation, and required SE constraints at user equipments. The formulated optimization problem is highly non-convex, and thus, it is difficult to obtain the global optimal solution. Therefore, we develop a simple yet efficient iterative algorithm for its solution. In addition, the performance of collocated massive MIMO-NOMA (COmMIMO-NOMA) system is also characterized. Numerical results are provided to show the superior performance of the proposed UC algorithms compared to baseline schemes. The effectiveness of applying NOMA in CFmMIMO and COmMIMO systems is also validated.

@article{9580734, author = {Quang Nhat Le and Van-Dinh Nguyen and Octavia A. Dobre and Nam-Phong Nguyen and Ruiqin Zhao and Symeon Chatzinotas}, journal = {IEEE Transactions on Vehicular Technology}, title = {Learning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks}, year = {2021}, volume = {70}, number = {12}, pages = {12872-12887}, doi = {10.1109/TVT.2021.3121217}, month = {Dec} }

Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

Vu, Chatzinotas, Nguyen, Hoang, Nguyen, Renzo & Ottersten
IEEE Transactions on Wireless Communications

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We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with $M$ RF chains and $N$ antennas, where $M < N$ . Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of $M$ antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.

@article{9337188, author = {Thang X. Vu and Symeon Chatzinotas and Van-Dinh Nguyen and Dinh Thai Hoang and Diep N. Nguyen and Marco Di Renzo and Bj{\"o}rn Ottersten}, journal = {IEEE Transactions on Wireless Communications}, title = {Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance}, year = {2021}, volume = {20}, number = {6}, pages = {3710-3722}, doi = {10.1109/TWC.2021.3052973}, month = {June} }

Resource Allocation for AF Relaying Wireless-Powered Networks With Nonlinear Energy Harvester

Nguyen, Nguyen, Pham, Lee & Kim
IEEE Communications Letters

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This letter considers a relay-based wireless-powered communication network to assist wireless communication between a source and multiple users. In particular, the relay adopts a nonlinear energy model to harvest energy from a power beacon and subsequently uses it for information transmission over time-division multiple access. Aiming at the maximization of end-to-end (e2e) sum throughput, we formulate a novel optimization problem that jointly optimizes the power and time fraction for energy and information transmission. For a simple yet efficient solution for the nonconvex problem, we first convert it to a more computationally tractable problem and then develop an iterative algorithm, in which closed-form solutions are obtained at each iteration. The effectiveness of our proposed approach is verified and demonstrated through simulation results. Moreover, the results reveal that the source should transmit with its maximum allowable power budget to obtain the optimal e2e sum throughput.

@article{9195847, author = {Tien-Tung Nguyen and Van-Dinh Nguyen and Quoc-Viet Pham and Jong-Ho Lee and Yong-Hwa Kim}, journal = {IEEE Communications Letters}, title = {Resource Allocation for AF Relaying Wireless-Powered Networks With Nonlinear Energy Harvester}, year = {2021}, volume = {25}, number = {1}, pages = {229-233}, doi = {10.1109/LCOMM.2020.3023937}, month = {Jan} }

Short-Packet Communications in Multi-Hop WPINs: Performance Analysis and Deep Learning Design

Nguyen, Nguyen, da Costa & An
2021 IEEE Global Communications Conference (GLOBECOM)

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In this paper, we study short-packet communications (SPCs) in multi-hop wireless-powered Internet-of-Things networks (WPINs), where IoT devices transmit short packets to multiple destination nodes by harvesting energy from multiple power beacons. To improve system block error rate (BLER) and throughput, we propose a best relay-best user (bR-bU) selection scheme with an accumulated energy harvesting mechanism. Closed-form expressions for the BLER and throughput of the proposed scheme over Rayleigh fading channels are derived and the respective asymptotic analysis is also carried out. To support real-time settings, we design a deep neural network (DNN) framework to predict the system throughput under different channel settings. Numerical results demonstrate that the proposed bR-bU selection scheme outperforms several baseline ones in terms of the BLER and throughput, showing to be an efficient strategy for multi-hop SPCs. The resulting DNN can estimate accurately the throughput with low execution time. The effects of message size on reliability and latency are also evaluated and discussed.

@inproceedings{9685765, author = {Toan-Van Nguyen and Van-Dinh Nguyen and Daniel Benevides {da Costa} and Beongku An}, booktitle = {2021 IEEE Global Communications Conference (GLOBECOM)}, title = {Short-Packet Communications in Multi-Hop WPINs: Performance Analysis and Deep Learning Design}, year = {2021}, pages = {1-6}, doi = {10.1109/GLOBECOM46510.2021.9685765}, month = {Dec} }

UAV-Assisted Secure Communications in Terrestrial Cognitive Radio Networks: Joint Power Control and 3D Trajectory Optimization

Nguyen, Nguyen, Nguyen & Shin
IEEE Transactions on Vehicular Technology

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This paper considers secure communications for an underlay cognitive radio network (CRN) in the presence of an external eavesdropper (Eve). The secrecy performance of CRNs is usually limited by the primary receiver's interference power constraint. To overcome this issue, we propose to use an unmanned aerial vehicle (UAV) as a friendly jammer to interfere Eve in decoding the confidential message from the secondary transmitter. Our goal is to jointly optimize the transmit power and UAV's trajectory in the three-dimensional space to maximize the average achievable secrecy rate of the secondary system. The formulated optimization problem is nonconvex due to the non-convexity of the objective and non-convexity of constraints, which is very challenging to solve. To obtain a suboptimal but efficient solution to the design problem, we first transform it into a more tractable form and then develop an iterative algorithm for its solution by leveraging the inner approximation (IA) framework. Combining tools from IA framework and S-procedure, we further extend the proposed algorithm to a more realistic scenario, where the imperfect location information of ground nodes (including Eve, secondary receiver and primary receiver) is considered, resulting in the average worst-case secrecy rate. Extensive numerical results are provided to demonstrate the merits of the proposed algorithms over existing approaches.

@article{9364745, author = {Phu X. Nguyen and Van-Dinh Nguyen and Hieu V. Nguyen and Oh-Soon Shin}, journal = {IEEE Transactions on Vehicular Technology}, title = {UAV-Assisted Secure Communications in Terrestrial Cognitive Radio Networks: Joint Power Control and 3D Trajectory Optimization}, year = {2021}, volume = {70}, number = {4}, pages = {3298-3313}, doi = {10.1109/TVT.2021.3062283}, month = {April} }

UAV-Enabled Cooperative Jamming for Physical Layer Security in Cognitive Radio Network

Nguyen, Nguyen, Nguyen & Shin
Autonomous Airborne Wireless Networks

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Summary This chapter proposes a new method that uses a cooperative unmanned aerial vehicle (UAV) as a friendly jammer to enhance the security performance of cognitive radio networks. Herein, a secondary transmitter (ST) sends confidential messages to a secondary receiver (SR) in the presence of an external eavesdropper (Eve), and the UAV acts as a friendly jammer that degrades the decoding capability of Eve. Therefore, resource allocation in such a network must jointly optimize the transmission power and the UAV's trajectory to maximize the secrecy rate, while satisfying a given interference threshold at the primary receiver (PR). The original problem is not convex, and thus, global optimality is difficult to achieve. To address this problem, we first transform it into a more tractable form and then propose a successive convex approximation-based algorithm for its solutions. The proposed algorithm has low computational complexity and is guaranteed to obtain at least a locally optimal solution to the original problem. Numerical results illustrate that the proposed method significantly improves the security performance of the cognitive radio networks compared to the previous methods.

@inbook{doi:https://doi.org/10.1002/9781119751717.ch7, author = {Phu X. Nguyen and Hieu V. Nguyen and Van-Dinh Nguyen and Oh-Soon Shin}, publisher = {John Wiley & Sons, Ltd}, title = {UAV-Enabled Cooperative Jamming for Physical Layer Security in Cognitive Radio Network}, booktitle = {Autonomous Airborne Wireless Networks}, chapter = {7}, pages = {119-140}, doi = {https://doi.org/10.1002/9781119751717.ch7}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119751717.ch7}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119751717.ch7}, year = {2021} }

2020

A Novel Heap-based Pilot Assignment for Full Duplex Cell-Free Massive MIMO with Zero-Forcing

Nguyen, Nguyen, Dobre, Sharma, Chatzinotas, Ottersten & Shin
ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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This paper investigates the combined benefits of full-duplex (FD) and cell-free massive multiple-input multiple-output (CF-mMIMO), where a large number of distributed access points (APs) having FD capability simultaneously serve numerous uplink and downlink user equipments (UEs) on the same time-frequency resources. To enable the incorporation of FD technology in CF-mMIMO systems, we propose a novel heap-based pilot assignment algorithm, which not only can mitigate the effects of pilot contamination but also reduce the involved computational complexity. Then, we formulate a robust design problem for spectral efficiency (SE) maximization in which the power control and AP-UE association are jointly optimized, resulting in a difficult mixed-integer nonconvex programming. To solve this problem, we derive a more tractable problem before developing a very simple iterative algorithm based on inner approximation method with polynomial computational complexity. Numerical results show that our proposed methods with realistic parameters significantly outperform the existing approaches in terms of the quality of channel estimate and SE.

@inproceedings{9149439, author = {Hieu V. Nguyen and Van-Dinh Nguyen and Octavia A. Dobre and Shree Krishna Sharma and Symeon Chatzinotas and Bj{\"o}rn Ottersten and Oh-Soon Shin}, booktitle = {ICC 2020 - 2020 IEEE International Conference on Communications (ICC)}, title = {A Novel Heap-based Pilot Assignment for Full Duplex Cell-Free Massive MIMO with Zero-Forcing}, year = {2020}, pages = {1-6}, doi = {10.1109/ICC40277.2020.9149439}, month = {June} }

Hybrid User Pairing for Spectral and Energy Efficiencies in Multiuser MISO-NOMA Networks With SWIPT

Nguyen, Nguyen, da Costa & An
IEEE Transactions on Communications

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In this paper, we propose a novel hybrid user pairing (HUP) scheme in multiuser multiple-input single-output non-orthogonal multiple access networks with simultaneous wireless information and power transfer. In this system, two information users with distinct channel conditions are optimally paired while energy users perform energy harvesting (EH) under non-linearity of the EH circuits. We consider the problem of jointly optimizing user pairing and power allocation to maximize the overall spectral efficiency (SE) and energy efficiency (EE) subject to user-specific quality-of-service and harvested power requirements. A new paradigm for the EE-EH trade-off is then proposed to achieve a good balance of network power consumption. Such design problems are formulated as the maximization of non-concave functions subject to the class of mixed-integer non-convex constraints, which are very challenging to solve optimally. To address these challenges, we first relax binary pairing variables to be continuous and transform the design problems into equivalent non-convex ones, but with more tractable forms. We then develop low-complexity iterative algorithms to improve the objectives and converge to a local optimum by means of the inner approximation framework. Simulation results show the convergence of proposed algorithms and the SE and EE improvements of the proposed HUP scheme over state-of-the-art designs. In addition, the effects of key parameters such as the number of antennas and dynamic power at the BS, target data rates, and energy threshold, on the system performance are evaluated to show the effectiveness of the proposed schemes in balancing resource utilization.

@article{9091839, author = {Toan-Van Nguyen and Van-Dinh Nguyen and Daniel Benevides {da Costa} and Beongku An}, journal = {IEEE Transactions on Communications}, title = {Hybrid User Pairing for Spectral and Energy Efficiencies in Multiuser MISO-NOMA Networks With SWIPT}, year = {2020}, volume = {68}, number = {8}, pages = {4874-4890}, doi = {10.1109/TCOMM.2020.2994204}, month = {Aug} }

On the Spectral and Energy Efficiencies of Full-Duplex Cell-Free Massive MIMO

Nguyen, Nguyen, Dobre, Sharma, Chatzinotas, Ottersten & Shin
IEEE Journal on Selected Areas in Communications

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In-band full-duplex (FD) operation is practically more suited for short-range communications such as WiFi and small-cell networks, due to its current practical limitations on the self-interference cancellation. In addition, cell-free massive multiple-input multiple-output (CF-mMIMO) is a new and scalable version of MIMO networks, which is designed to bring service antennas closer to end user equipments (UEs). To achieve higher spectral and energy efficiencies (SE-EE) of a wireless network, it is of practical interest to incorporate FD capability into CF-mMIMO systems to utilize their combined benefits. We formulate a novel and comprehensive optimization problem for the maximization of SE and EE in which power control, access point-UE (AP-UE) association and AP selection are jointly optimized under a realistic power consumption model, resulting in a difficult class of mixed-integer nonconvex programming. To tackle the binary nature of the formulated problem, we propose an efficient approach by exploiting a strong coupling between binary and continuous variables, leading to a more tractable problem. In this regard, two low-complexity transmission designs based on zero-forcing (ZF) are proposed. Combining tools from inner approximation framework and Dinkelbach method, we develop simple iterative algorithms with polynomial computational complexity in each iteration and strong theoretical performance guaranteed. Furthermore, towards a robust design for FD CF-mMIMO, a novel heap-based pilot assignment algorithm is proposed to mitigate effects of pilot contamination. Numerical results show that our proposed designs with realistic parameters significantly outperform the well-known approaches (i.e., small-cell and collocated mMIMO) in terms of the SE and EE. Notably, the proposed ZF designs require much less execution time than the simple maximum ratio transmission/combining.

@article{9110914, author = {Hieu V. Nguyen and Van-Dinh Nguyen and Octavia A. Dobre and Shree Krishna Sharma and Symeon Chatzinotas and Bj{\"o}rn Ottersten and Oh-Soon Shin}, journal = {IEEE Journal on Selected Areas in Communications}, title = {On the Spectral and Energy Efficiencies of Full-Duplex Cell-Free Massive MIMO}, year = {2020}, volume = {38}, number = {8}, pages = {1698-1718}, doi = {10.1109/JSAC.2020.3000810}, month = {Aug} }

Optimization of Rate Fairness in Multi-Pair Wireless-Powered Relaying Systems

Bui, Nguyen, Nguyen, Dobre & Shin
IEEE Communications Letters

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This letter considers a multi-pair decode-and-forward relay network where a power-splitting (PS) protocol is adopted at the energy-constrained relay to provide simultaneous wireless information and energy harvesting (EH). To achieve higher efficiency of EH, we propose a new PS-based EH architecture at the relay by incorporating an alternating current (AC) computing logic, which is employed to directly use the wirelessly harvested AC energy for computational blocks. Under a nonlinear EH circuit, our goal is to maximize the fairness of end-to-end rate among user pairs subject to power constraints, resulting in a non-convex problem. We propose an iterative algorithm to achieve a suboptimal and efficient solution to this challenging problem by leveraging the inner approximation framework. Numerical results demonstrate that the proposed algorithm outperforms the traditional direct current computing and other baseline schemes.

@article{8936852, author = {Van-Phuc Bui and Van-Dinh Nguyen and Hieu V. Nguyen and Octavia A. Dobre and Oh-Soon Shin}, journal = {IEEE Communications Letters}, title = {Optimization of Rate Fairness in Multi-Pair Wireless-Powered Relaying Systems}, year = {2020}, volume = {24}, number = {3}, pages = {603-607}, doi = {10.1109/LCOMM.2019.2960350}, month = {March} }

Resource Allocation for Energy Efficiency in OFDMA-Enabled WPCN

Nguyen, Pham, Nguyen, Lee & Kim
IEEE Wireless Communications Letters

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This letter considers a wireless powered communication network (WPCN), where an energy-constrained device directly uses harvested energy from a power transfer source to transmit independent signals to multiple Internet of Things (IoT) users using orthogonal frequency division multiple access (OFDMA). Our goal is to maximize the system energy efficiency (EE) by jointly optimizing the duration of energy harvesting (EH), subcarrier and power allocation. The formulated problem is a mixed integer nonlinear programming (MINLP) problem due to the presence of binary assignment variables, and thus it is very challenging to solve it directly. By leveraging Dinkelbach method, a very efficient iterative algorithm with closed-form solutions in each iteration is developed, where its convergence is guaranteed. Numerical results show that the proposed algorithm obtains a fast convergence and outperforms baseline algorithms. Notably, they also reveal that the power source should transmit its maximum allowable power to obtain the optimal EE performance.

@article{9149891, author = {Tien-Tung Nguyen and Quoc-Viet Pham and Van-Dinh Nguyen and Jong-Ho Lee and Yong-Hwa Kim}, journal = {IEEE Wireless Communications Letters}, title = {Resource Allocation for Energy Efficiency in OFDMA-Enabled WPCN}, year = {2020}, volume = {9}, number = {12}, pages = {2049-2053}, doi = {10.1109/LWC.2020.3012206}, month = {Dec} }

Resource Allocation for UAV Relay-Assisted IoT Communication Networks

Tran, Nguyen, Gautam, Chatzinotas, Vu & Ottersten
2020 IEEE Globecom Workshops (GC Wkshps

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This work studies unmanned aerial vehicle (UAV) relay-assisted Internet of Things (IoT) communication networks in which a UAV is deployed as an aerial base station (BS) to collect time-constrained data from IoT devices and transfer information to a ground gateway (GW). In this context, we jointly optimize the allocated bandwidth, transmission power, as well as the UAV trajectory to maximize the total system throughput while satisfying the user’s latency requirement and the UAV’s limited storage capacity. The formulated problem is strongly non-convex which is very challenging to solve optimally. Towards an appealing solution, we first introduce new variables to convert the original problem into a computationally tractable form, and then develop an iterative algorithm for its solution by leveraging the inner approximation method. Numerical results are given to show significant performance improvement over benchmark schemes.

@inproceedings{9367522, author = {Dinh-Hieu Tran and Van-Dinh Nguyen and Sumit Gautam and Symeon Chatzinotas and Thang X. Vu and Bj{\"o}rn Ottersten}, booktitle = {2020 IEEE Globecom Workshops (GC Wkshps}, title = {Resource Allocation for UAV Relay-Assisted IoT Communication Networks}, year = {2020}, pages = {1-7}, doi = {10.1109/GCWkshps50303.2020.9367522}, month = {Dec} }

2019

A Novel Spectral-Efficient Resource Allocation Approach for NOMA-Based Full-Duplex Systems

Nguyen, Nguyen, Dobre, Nguyen, Dutkiewicz & Shin
2019 IEEE Global Communications Conference (GLOBECOM)

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This paper investigates the coexistence of non- orthogonal multiple access (NOMA) and full-duplex (FD), where the NOMA successive interference cancellation technique is applied simultaneously to both uplink (UL) and downlink (DL) transmissions in the same time-frequency resource block. Specifically, we jointly optimize the user association (UA) and power control to maximize the overall sum rate, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize the UL users' decoding order and the DL users' clustering, which results in a mixed-integer non- convex problem. For solving this problem, we first relax the binary variables to be continuous, and then propose a low-complexity design based on the combination of the inner convex approximation framework and the penalty method. Numerical results show that the proposed algorithm significantly outperforms the conventional FD-based schemes, FD-NOMA and its half-duplex counterpart with random UA.

@inproceedings{9013457, author = {Hieu V. Nguyen and Van-Dinh Nguyen and Octavia A. Dobre and Diep N. Nguyen and Eryk Dutkiewicz and Oh-Soon Shin}, booktitle = {2019 IEEE Global Communications Conference (GLOBECOM)}, title = {A Novel Spectral-Efficient Resource Allocation Approach for NOMA-Based Full-Duplex Systems}, year = {2019}, pages = {1-6}, doi = {10.1109/GLOBECOM38437.2019.9013457}, month = {Dec} }

An Efficient Design for NOMA-Assisted MISO-SWIPT Systems with AC Computing

Nguyen & Shin
IEEE Access

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We consider a multiple-input single-output simultaneous wireless information and power transfer (MISO-SWIPT) system, where a power-splitting protocol is employed at users near the base station (BS) to provide both energy harvesting (EH) and information decoding. For the considered system, it is of practical interest to adopt non-orthogonal multiple access (NOMA) to improve the network spectral efficiency, while still meeting the EH requirements. In addition, an alternating current computing (ACC) logic is incorporated into EH receivers to directly use the wirelessly harvested AC power, which in turn achieves higher energy efficiency than traditional direct current computing (DCC). We formulate a problem of maximizing the spectral efficiency subject to the constraints of quality-of-service for the individual user, EH requirements, and BS's maximum transmit power, where the beamformers and PS ratios are jointly optimized. To achieve an efficient solution to this nonconvex problem, we propose an iterative algorithm based on the inner approximation (IA) framework, where the approximate convex problem solved in each iteration can be cast as a second-order-cone program with convergence guaranteed. To further simplify the problem design, we propose a zero-forcing beamforming-based NOMA approach to partially eliminate interference, which has the potential to significantly reduce the number of variables. The extensive numerical results are presented to demonstrate the effectiveness of the proposed algorithms, compared with the baseline schemes.

@article{8763935, author = {Van-Dinh Nguyen and Oh-Soon Shin}, journal = {IEEE Access}, title = {An Efficient Design for NOMA-Assisted MISO-SWIPT Systems with AC Computing}, year = {2019}, volume = {7}, pages = {97094-97105}, doi = {10.1109/ACCESS.2019.2928877} }

Joint Antenna Array Mode Selection and User Assignment for Full-Duplex MU-MISO Systems

Nguyen, Nguyen, Dobre, Wu & Shin
IEEE Transactions on Wireless Communications

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This paper considers a full-duplex (FD) multiuser multiple-input single-output system where a base station simultaneously serves both uplink (UL) and downlink (DL) users on the same time-frequency resource. The crucial barriers in implementing FD systems reside in the residual self-interference and co-channel interference. To accelerate the use of FD radio in future wireless networks, we aim at managing the network interference more effectively by jointly designing the selection of half-array antenna modes (in the transmit or receive mode) at the base station with time phases and user assignments. The first problem of interest is to maximize the overall sum rate subject to quality-of-service requirements, which is formulated as a highly non-concave utility function followed by non-convex constraints. To address the design problem, we propose an iterative low-complexity algorithm by developing new inner approximations, and its convergence to a stationary point is guaranteed. To provide more insights into the solution of the proposed design, a general max-min rate optimization is further considered to maximize the minimum per-user rate while satisfying a given ratio between UL and DL rates. Furthermore, a robust algorithm is devised to verify that the proposed scheme works well under channel uncertainty. The simulation results demonstrate that the proposed algorithms exhibit fast convergence and substantially outperform existing schemes.

@article{8680764, author = {Hieu V. Nguyen and Van-Dinh Nguyen and Octavia A. Dobre and Yongpeng Wu and Oh-Soon Shin}, journal = {IEEE Transactions on Wireless Communications}, title = {Joint Antenna Array Mode Selection and User Assignment for Full-Duplex MU-MISO Systems}, year = {2019}, volume = {18}, number = {6}, pages = {2946-2963}, doi = {10.1109/TWC.2019.2907489}, month = {June} }

Joint Power Control and User Association for NOMA-Based Full-Duplex Systems

Nguyen, Nguyen, Dobre, Nguyen, Dutkiewicz & Shin
IEEE Transactions on Communications

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This paper investigates the coexistence of non-orthogonal multiple access (NOMA) and full-duplex (FD) to improve both spectral efficiency (SE) and user fairness. In such a scenario, NOMA based on the successive interference cancellation technique is simultaneously applied to both uplink (UL) and downlink (DL) transmissions in an FD system. We consider the problem of jointly optimizing user association (UA) and power control to maximize the overall SE, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize UL users’ decoding order and DL users’ clustering, which results in a mixed-integer non-convex problem. For practically appealing applications, we first relax the binary variables and then propose two low-complexity designs. In the first design, the continuous relaxation problem is solved using the inner convex approximation framework. Next, we additionally introduce the penalty method to further accelerate the performance of the former design. For a benchmark, we develop an optimal solution based on brute-force search (BFS) over all possible cases of UAs. It is demonstrated in numerical results that the proposed algorithms outperform the conventional FD-based schemes and its half-duplex counterpart, as well as yield data rates close to those obtained by BFS-based algorithm.

@article{8788625, author = {Hieu V. Nguyen and Van-Dinh Nguyen and Octavia A. Dobre and Diep N. Nguyen and Eryk Dutkiewicz and Oh-Soon Shin}, journal = {IEEE Transactions on Communications}, title = {Joint Power Control and User Association for NOMA-Based Full-Duplex Systems}, year = {2019}, volume = {67}, number = {11}, pages = {8037-8055}, doi = {10.1109/TCOMM.2019.2933217}, month = {Nov} }

Optimal User Pairing for Achieving Rate Fairness in Downlink NOMA Networks

Bui, Nguyen, Nguyen, Nguyen & Shin
2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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In this paper, a downlink non-orthogonal multiple access (NOMA) network is studied. We investigate the problem of jointly optimizing user pairing and beam-forming design to maximize the minimum rate among all users. The considered problem belongs to a difficult class of mixed-integer nonconvex optimization programming. We first relax the binary constraints and adopt sequential convex approximation method to solve the relaxed problem, which is guaranteed to converge at least to a locally optimal solution. Numerical results show that the proposed method attains higher rate fairness among users, compared with traditional beamforming solutions, i.e., random pairing NOMA and beamforming systems.

@inproceedings{8669061, author = {Van-Phuc Bui and Phu X. Nguyen and Hieu V. Nguyen and Van-Dinh Nguyen and Oh-Soon Shin}, booktitle = {2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)}, title = {Optimal User Pairing for Achieving Rate Fairness in Downlink NOMA Networks}, year = {2019}, pages = {575-578}, doi = {10.1109/ICAIIC.2019.8669061}, month = {Feb} }

Simultaneous Uplink and Downlink Transmissions for Wireless Powered Communication Networks

Do, Nguyen, Shin & An
IEEE Communications Letters

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In this letter, we propose a novel simultaneous uplink (UL) and downlink (DL) transmission scheme aiming to achieve a higher spectral efficiency for wireless power communication networks (WPCNs). Specifically, by exploiting full-duplex (FD) radio and wireless power transfer techniques, an FD hybrid access point is able to communicate with a wireless powered UL user and a DL user over the same time-frequency resource. The optimization problem of interest is to maximize the sum rate of the scheme under a given power budget, which is formulated as a nonconvex problem. To this end, we propose a low-complexity path following algorithm for its solution. Numerical results reveal that the proposed scheme indeed achieves substantial sum-rate improvement for the WPCNs.

@article{8565946, author = {Tri-Nhu Do and Van-Dinh Nguyen and Oh-Soon Shin and Beongku An}, journal = {IEEE Communications Letters}, title = {Simultaneous Uplink and Downlink Transmissions for Wireless Powered Communication Networks}, year = {2019}, volume = {23}, number = {2}, pages = {374-377}, doi = {10.1109/LCOMM.2018.2885303}, month = {Feb} }

Spectral Efficiency Maximization for Multiuser MISO-NOMA Downlink Systems with SWIPT

Nguyen, Nguyen, Do, da Costa & An
2019 IEEE Global Communications Conference (GLOBECOM)

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In this paper, we study the problem of jointly optimizing user pairing and beamforming design in multiuser multiple-input single-output (MU-MISO) non-orthogonal multiple access (NOMA) downlink systems with simultaneous wireless information and power transfer (SWIPT). Aiming at maximizing the achievable sum throughput subject to energy harvesting (EH) constraints, we propose a hybrid user pairing beamforming scheme (HBS), where two users with distinct channel conditions are optimally selected to perform user pairing. Moreover, we adopt a non-linear EH model for energy users to reveal their practical circuit characteristics. The sum throughput problem is formulated as a class of mixed-integer nonconvex optimization programming which is computationally prohibitive. To solve this challenging problem, we propose a low-complexity iterative algorithm, yet efficient, based on sequential convex approximation method to arrive at least the local optima. Numerical results are provided to demonstrate the performance improvement of the proposed HBS scheme over the multiuser beamforming one without user pairing, revealing to be an effective scheme for MU-MISO-NOMA downlink systems.

@inproceedings{9014339, author = {Toan-Van Nguyen and Van-Dinh Nguyen and Tri-Nhu Do and Daniel Benevides {da Costa} and Beongku An}, booktitle = {2019 IEEE Global Communications Conference (GLOBECOM)}, title = {Spectral Efficiency Maximization for Multiuser MISO-NOMA Downlink Systems with SWIPT}, year = {2019}, pages = {1-6}, doi = {10.1109/GLOBECOM38437.2019.9014339}, month = {Dec} }

Sum Rate Maximization for Multi-User Wireless Powered IoT Network With Non-Linear Energy Harvester: Time and Power Allocation

Nguyen, Nguyen, Lee & Kim
IEEE Access

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We consider the “harvest-then-transmit” protocol in a wireless powered communication network (WPCN), where an energy-constrained access point (AP) harvests energy from the radio-frequency signals transmitted by a power beacon (PB) for assisting user data transmission. In the wireless information transfer (WIT) phase, AP employs the harvested energy to convey independent signals to multiple users through either time-division multiple access (TDMA) or orthogonal frequency-division multiple access (OFDMA). Aiming to maximize the sum rate (SR) of the WPCN, we jointly optimize the energy harvesting (EH) time and the AP power allocation, considering both the conventional linear and practical nonlinear EH models at the AP. The optimization problems of both TDMA- and OFDMA-enabled WPCNs are formulated as nonconvex programs, which are challenging to solve globally. To achieve an efficient optimal solution to the problem of TDMA-enabled WPCN, we first decompose the original nonconvex problem into three convex subproblems, and then propose a low-complexity iterative algorithm for its solution. For the OFDMA-enabled WPCN, the problem belongs to a difficult class of mixed-integer nonconvex programming due to the involvement of binary variables for subcarrier allocation. To overcome this issue, we convert the problem to a quasi-convex problem and then employ a bisection search to obtain the optimal solution. Simulation results are provided to confirm the benefit of jointly optimizing the EH time and the AP power allocation compared to baseline schemes. The performance of the proposed TDMA-enabled WPCN is shown to be superior to that of the proposed OFDMA-enabled WPCN in terms of SR when the transmit power of PB and the number of antennas of AP are relatively large.

@article{8868074, author = {Tien-Tung Nguyen and Van-Dinh Nguyen and Jong-Ho Lee and Yong-Hwa Kim}, journal = {IEEE Access}, title = {Sum Rate Maximization for Multi-User Wireless Powered IoT Network With Non-Linear Energy Harvester: Time and Power Allocation}, year = {2019}, volume = {7}, pages = {149698-149710}, doi = {10.1109/ACCESS.2019.2947321} }

Tag Cardinality Estimation Using Expectation-Maximization in ALOHA-Based RFID Systems With Capture Effect and Detection Error

Nguyen, Nguyen & Pham
IEEE Wireless Communications Letters

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Tag cardinality estimation is one of the most crucial issues in radio frequency identification technology. The issue, however, usually faces with challenges in wireless fading environments due to the presence of the so-called capture effect (CE) and detection error (DE). The aim of this letter is to provide an efficient and accurate estimation method to cope with the CE and DE using expectation-maximization algorithm and the standard Aloha-based protocol. We show that the proposed method gives more accurate estimates than a conventional one. Thanks to this fact, the Aloha frame size used for the tag identification process can also be optimally selected so that the identification efficiency can be improved. Computer simulations are presented to confirm the merit of the proposed method.

@article{8599016, author = {Chuyen T. Nguyen and Van-Dinh Nguyen and Anh T. Pham}, journal = {IEEE Wireless Communications Letters}, title = {Tag Cardinality Estimation Using Expectation-Maximization in ALOHA-Based RFID Systems With Capture Effect and Detection Error}, year = {2019}, volume = {8}, number = {2}, pages = {636-639}, doi = {10.1109/LWC.2018.2890650}, month = {April} }

UAV-Enabled Jamming Noise for Achieving Secure Communications in Cognitive Radio Networks

Nguyen, Nguyen, Nguyen & Shin
2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)

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In this paper, physical layer security is considered for cognitive radio networks using an unmanned aerial vehicle (UAV)-enabled jamming noise. In the studied model, a secondary transmitter sends confidential messages to a secondary receiver in the presence of an external eavesdropper (Eve), and the UAV acts as a friendly jammer that degrades the decoding capability of Eve. Therefore, resource allocation in such a network must jointly optimize the transmission power and UAV's trajectory to maximize the secrecy rate, while satisfying a given interference threshold at the primary receiver. The design problem is non-convex, and thus, global optimality is difficult to obtain. Aiming to solve this problem, we first transform it into a more tractable form, and then propose a successive convex approximation-based algorithm for its solutions. The proposed algorithm has a low computational complexity and is guaranteed to obtain at least a locally optimal solution of the original problem. Numerical results are provided to demonstrate the effectiveness of the proposed design, compared to the existing ones.

@inproceedings{8651678, author = {Phu X. Nguyen and Hieu V. Nguyen and Van-Dinh Nguyen and Oh-Soon Shin}, booktitle = {2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)}, title = {UAV-Enabled Jamming Noise for Achieving Secure Communications in Cognitive Radio Networks}, year = {2019}, pages = {1-6}, doi = {10.1109/CCNC.2019.8651678}, month = {Jan} }

2018

A New Design Paradigm for Secure Full-Duplex Multiuser Systems

Nguyen, Nguyen, Dobre & Shin
IEEE Journal on Selected Areas in Communications

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We consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to simultaneously serve both downlink (DL) and uplink (UL) users in the presence of half-duplex eavesdroppers (Eves). The problem is to maximize the minimum (max-min) secrecy rate (SR) among all legitimate users, where the information signals at the FD-BS are accompanied with artificial noise to debilitate the Eves' channels. To enhance the max-min SR, a major part of the power budget should be allocated to serve the users with poor channel qualities, such as those far from the FD-BS, undermining the SR for other users, and thus compromising the SR per-user. In addition, the main obstacle in designing an FD system is due to the self-interference (SI) and co-channel interference (CCI) among users. We therefore propose an alternative solution, where the FD-BS uses a fraction of the time block to serve near DL users and far UL users, and the remaining fractional time to serve other users. The proposed scheme mitigates the harmful effects of SI, CCI, and multiuser interference, and provides system robustness. The SR optimization problem has a highly nonconcave and nonsmooth objective, subject to nonconvex constraints. For the case of perfect channel state information (CSI), we develop a low-complexity path-following algorithm, which involves only a simple convex program of moderate dimension at each iteration. We show that our path-following algorithm guarantees convergence at least to a local optimum. Then, we extend the path-following algorithm to the cases of partially known Eves' CSI, where only statistics of CSI for the Eves are known, and worst-case scenario in which Eves can employ a more advanced linear decoder. The merit of our proposed approach is further demonstrated by extensive numerical results.

@article{8333690, author = {Van-Dinh Nguyen and Hieu V. Nguyen and Octavia A. Dobre and Oh-Soon Shin}, journal = {IEEE Journal on Selected Areas in Communications}, title = {A New Design Paradigm for Secure Full-Duplex Multiuser Systems}, year = {2018}, volume = {36}, number = {7}, pages = {1480-1498}, doi = {10.1109/JSAC.2018.2824379}, month = {July} }

Achieving Rate Fairness in Multipair Full-Duplex Relaying Systems

Kim, Nguyen, Kang & Shin
2018 International Conference on Information and Communication Technology Convergence (ICTC)

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We consider a decode-and-forward full-duplex relaying system for multiple pairs of users. We propose a suboptimal solution to solve the optimization problem design which aims to maximize minimum achievable rate for all user pairs.

@inproceedings{8539347, author = {Hyeon Min Kim and Van-Dinh Nguyen and Gil-mo Kang and Oh-Soon Shin}, booktitle = {2018 International Conference on Information and Communication Technology Convergence (ICTC)}, title = {Achieving Rate Fairness in Multipair Full-Duplex Relaying Systems}, year = {2018}, pages = {1185-1185}, doi = {10.1109/ICTC.2018.8539347}, month = {Oct} }

Cooperative Prediction-and-Sensing-Based Spectrum Sharing in Cognitive Radio Networks

Nguyen & Shin
IEEE Transactions on Cognitive Communications and Networking

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This paper proposes prediction-and-sensing-based spectrum sharing, a new spectrum-sharing model for cognitive radio networks, with a time structure for each resource block divided into a spectrum prediction-and-sensing phase and a data transmission phase. Cooperative spectrum prediction is incorporated as a sub-phase of spectrum sensing in the first phase. We investigate a joint design of transmit beamforming at the secondary base station (BS) and sensing time. The primary design goal is to maximize the sum rate of all secondary users (SUs) subject to the minimum rate requirement for all SUs, the transmit power constraint at the secondary BS, and the interference power constraints at all primary users. The original problem is difficult to solve since it is highly nonconvex. We first convert the problem into a more tractable form, then arrive at a convex program based on an inner approximation framework, and finally propose a new algorithm to successively solve this convex program. We prove that the proposed algorithm iteratively improves the objective while guaranteeing convergence at least to local optima. Simulation results demonstrate that the proposed algorithm reaches a stationary point after only a few iterations with a substantial performance improvement over existing approaches.

@article{8116681, author = {Van-Dinh Nguyen and Oh-Soon Shin}, journal = {IEEE Transactions on Cognitive Communications and Networking}, title = {Cooperative Prediction-and-Sensing-Based Spectrum Sharing in Cognitive Radio Networks}, year = {2018}, volume = {4}, number = {1}, pages = {108-120}, doi = {10.1109/TCCN.2017.2776138}, month = {March} }

On the Design of Secure Full-Duplex Multiuser Systems under User Grouping Method

Nguyen, Nguyen, Dobre & Shin
2018 IEEE International Conference on Communications (ICC)

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Consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to concurrently serve both downlink and uplink users in the presence of half-duplex eavesdroppers (Eves). The target problem is to maximize the minimum secrecy rate (SR) among all legitimate users. A novel user grouping-based fractional time allocation is proposed as an alternative solution, where information signals at the FD-BS are accompanied by artificial noise to degrade the Eves' channels. The SR problem has a highly non-concave and non-smooth objective, subject to non-convex constraints due to coupling between the optimization variables. Nevertheless, we develop a path-following low- complexity algorithm, which involves only a simple convex program of moderate dimensions at each iteration. Numerical results demonstrate the merit of the proposed approach compared to existing well-known ones, i.e., conventional FD and FD non-orthogonal multiple access.

@inproceedings{8422451, author = {Van-Dinh Nguyen and Hieu V. Nguyen and Octavia A. Dobre and Oh-Soon Shin}, booktitle = {2018 IEEE International Conference on Communications (ICC)}, title = {On the Design of Secure Full-Duplex Multiuser Systems under User Grouping Method}, year = {2018}, pages = {1-6}, doi = {10.1109/ICC.2018.8422451}, month = {May} }

Uplink training for pilot decontamination in a multiceli massive MIMO system

Nguyen, Nguyen & Shinï
2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom)

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In this paper, we investigate pilot contamination in a multiceli massive multiple-input multiple-output (MIMO) system, in which the estimated channel state information (CSI) suffers a crucial difference from the real CSI. Pilot contamination is an obstacle to the achievable rate for users. We propose an uplink training strategy to limit the effect of intercell pilot contamination on channel estimation with low complexity. As a result, the downlink data rate through the reciprocity channels is significantly improved, which is verified by numerical results.

@inproceedings{8325813, author = {Hieu V. Nguyen and Van-Dinh Nguyen and Oh-Soon Shin{\"\i}}, booktitle = {2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom)}, title = {Uplink training for pilot decontamination in a multiceli massive MIMO system}, year = {2018}, pages = {97-101}, doi = {10.1109/SIGTELCOM.2018.8325813}, month = {Jan} }

2017

An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks With Interference Constraints

Nguyen, Tran, Duong, Shin & Farrell
IEEE Transactions on Vehicular Technology

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We consider a linear precoder design for an underlay cognitive radio multiple-input multiple-output (MIMO) broadcast channel, where the secondary system consisting of a secondary base station (BS) and a group of secondary users is allowed to share the same spectrum with the primary system. All the transceivers are equipped with multiple antennas, each of which has its own maximum power constraint. Assuming zero-forcing (ZF) method to eliminate the multiuser interference, we study the sum rate maximization problem for the secondary system subject to both per-antenna power constraints at the secondary BS and the interference power constraints at the primary users. The problem of interest differs from the ones studied previously that often assumed a sum power constraint and/or single antenna employed at either both the primary and secondary receivers or the primary receivers. To develop an efficient numerical algorithm, we first invoke the rank relaxation method to transform the considered problem into a convex-concave problem based on a downlink-uplink result. We then propose a barrier interior-point method to solve the resulting saddle point problem. In particular, in each iteration of the proposed method we find the Newton step by solving a system of discrete-time Sylvester equations, which help reduce the complexity significantly, compared to the conventional method. Simulation results are provided to demonstrate fast convergence and effectiveness of the proposed algorithm.

@article{7552576, author = {Van-Dinh Nguyen and Le-Nam Tran and Trung Q. Duong and Oh-Soon Shin and Ronan Farrell}, journal = {IEEE Transactions on Vehicular Technology}, title = {An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks With Interference Constraints}, year = {2017}, volume = {66}, number = {5}, pages = {3991-4004}, doi = {10.1109/TVT.2016.2602844}, month = {May} }

Convex Quadratic Programming for Maximizing Sum Throughput in MIMO-NOMA Multicell Networks

Nguyen, Tuan, Duong, Poor & Shin
GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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This paper aims to design linear precoders for signal superposition at the base stations of non- orthogonal multiple access multiple-input multiple-output multi-cellular systems to maximize the overall sum throughput subject to the users' quality-of-service requirements, which are imposed independently on the users' channel conditions. This design problem is formulated as the maximization of a highly nonlinear and nonsmooth function subject to nonconvex constraints, which is very computationally challenging. A path- following algorithm for its solution, which invokes only a simple convex problem of moderate dimension at each iteration, is developed. Generating a sequence of improved points, this algorithm converges at least to a local optimum. Numerical results are then provided to demonstrate its merit.

@inproceedings{8254177, author = {Van-Dinh Nguyen and Hoang D. Tuan and Trung Q. Duong and H. Vincent Poor and Oh-Soon Shin}, booktitle = {GLOBECOM 2017 - 2017 IEEE Global Communications Conference}, title = {Convex Quadratic Programming for Maximizing Sum Throughput in MIMO-NOMA Multicell Networks}, year = {2017}, pages = {1-6}, doi = {10.1109/GLOCOM.2017.8254177}, month = {Dec} }

Enhancing PHY Security of Cooperative Cognitive Radio Multicast Communications

Nguyen, Duong, Shin, Nallanathan & Karagiannidis
IEEE Transactions on Cognitive Communications and Networking

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In this paper, we propose a cooperative approach to improve the security of both primary and secondary systems in cognitive radio multicast communications. During their access to the frequency spectrum licensed to the primary users, the secondary unlicensed users assist the primary system in fortifying security by sending a jamming noise to the eavesdroppers, while simultaneously protect themselves from eavesdropping. The main objective of this paper is to maximize the secrecy rate of the secondary system, while adhering to all individual primary users' secrecy rate constraints. In the case of active eavesdroppers and perfect channel state information (CSI) at the transceivers, the utility function of interest is nonconcave and the involved constraints are nonconvex, and thus, the optimal solutions are troublesome. To solve this problem, we propose an iterative algorithm to arrive at least to a local optimum of the original nonconvex problem. This algorithm is guaranteed to achieve a Karush-Kuhn-Tucker solution. Then, we extend the optimization approach to the case of passive eavesdroppers and imperfect CSI knowledge at the transceivers, where the constraints are transformed into a linear matrix inequality and convex constraints, in order to facilitate the optimal solution.

@article{8024002, author = {Van-Dinh Nguyen and Trung Q. Duong and Oh-Soon Shin and Arumugam Nallanathan and George K. Karagiannidis}, journal = {IEEE Transactions on Cognitive Communications and Networking}, title = {Enhancing PHY Security of Cooperative Cognitive Radio Multicast Communications}, year = {2017}, volume = {3}, number = {4}, pages = {599-613}, doi = {10.1109/TCCN.2017.2748132}, month = {Dec} }

Joint Beamforming and Antenna Selection for Sum Rate Maximization in Cognitive Radio Networks

Nguyen, Nguyen, Nguyen & Shin
IEEE Communications Letters

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This letter studies joint transmit beamforming and antenna selection at a secondary base station (BS) with multiple primary users (PUs) in an underlay cognitive radio multiple-input single-output broadcast channel. The objective is to maximize the sum rate subject to the secondary BS transmit power, minimum required rates for secondary users, and PUs' interference power constraints. The utility function of interest is nonconcave and the involved constraints are nonconvex, so this problem is hard to solve. Nevertheless, we propose a new iterative algorithm that finds local optima at the least. We use an inner approximation method to construct and solve a simple convex quadratic program of moderate dimension at each iteration of the proposed algorithm. Simulation results indicate that the proposed algorithm converges quickly and outperforms existing approaches.

@article{7873235, author = {Van-Dinh Nguyen and Chuyen T. Nguyen and Hieu V. Nguyen and Oh-Soon Shin}, journal = {IEEE Communications Letters}, title = {Joint Beamforming and Antenna Selection for Sum Rate Maximization in Cognitive Radio Networks}, year = {2017}, volume = {21}, number = {6}, pages = {1369-1372}, doi = {10.1109/LCOMM.2017.2679186}, month = {June} }

Joint Fractional Time Allocation and Beamforming for Downlink Multiuser MISO Systems

Nguyen, Tuan, Duong, Shin & Poor
IEEE Communications Letters

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It is well known that the use of traditional transmit beamforming at a base station (BS) to manage interference in serving multiple users is effective only when the number of users is less than the number of transmit antennas at the BS. Non-orthogonal multiple access (NOMA) can improve the throughput of users with poorer channel conditions by compromising their own privacy, because other users with better channel conditions can decode the information of users with poorer channel conditions. NOMA still prefers that the number of users is less than the number of antennas at the BS transmitter. This letter resolves such issues by allocating separate fractional time slots for serving users with similar channel conditions. This enables the BS to serve more users within a time unit while the privacy of each user is preserved. The fractional times and beamforming vectors are jointly optimized to maximize the system's throughput. An efficient path-following algorithm, which invokes a simple convex quadratic program at each iteration, is proposed for the solution of this challenging optimization problem. Numerical results confirm its versatility.

@article{8023773, author = {Van-Dinh Nguyen and Hoang Duong Tuan and Trung Q. Duong and Oh-Soon Shin and H. Vincent Poor}, journal = {IEEE Communications Letters}, title = {Joint Fractional Time Allocation and Beamforming for Downlink Multiuser MISO Systems}, year = {2017}, volume = {21}, number = {12}, pages = {2650-2653}, doi = {10.1109/LCOMM.2017.2747544}, month = {Dec} }

Joint NOMA beamforming and user scheduling for sum rate maximization in a full-duplex system

Nguyen, Nguyen & Shin
2017 International Conference on Information and Communication Technology Convergence (ICTC)

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We consider a full-duplex (FD) system where the uplink (UL) and downlink (DL) users are simultaneously served in the same bandwidth. We propose a new design that incorporates the power allocation for UL users and a joint beamforming and non-orthogonal multiple access for DL users under the effects of both residual self-interference and cochannel interference. In particular, a sum rate maximization problem subject to transmit power constraints and per-user quality of service requirements is introduced. An approximation method is used to efficiently solve the problem. Simulation results verify that the proposed FD scheme outperforms a half-duplex and a conventional FD schemes.

@inproceedings{8190766, author = {Hieu V. Nguyen and Van-Dinh Nguyen and Oh-Soon Shin}, booktitle = {2017 International Conference on Information and Communication Technology Convergence (ICTC)}, title = {Joint NOMA beamforming and user scheduling for sum rate maximization in a full-duplex system}, year = {2017}, pages = {731-733}, doi = {10.1109/ICTC.2017.8190766}, month = {Oct} }

Low-Complexity Precoding for Sum Rate Maximization in Downlink Massive MIMO Systems

Nguyen, Nguyen & Shin
IEEE Wireless Communications Letters

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We propose a precoding scheme to improve the downlink sum rate for a multicell massive multiple-input multiple-output (MIMO) system. We first present a low-complexity approach based on dirty paper coding and zero-forcing that combines a reduced form of QR decomposition and an orthogonal projection. We formulate a downlink sum rate optimization problem that takes both intracell and intercell interference into account, and then we use the convex conjugate to transform the problem into an unconstrained dual problem to find an optimal solution by applying a quasi-Newton algorithm with low complexity per iteration. We prove that the proposed algorithm exhibits faster convergence than other methods, and the numerical results verify that the proposed precoding design outperforms conventional precoding methods in multicell massive MIMO systems.

@article{7812648, author = {Hieu V. Nguyen and Van-Dinh Nguyen and Oh-Soon Shin}, journal = {IEEE Wireless Communications Letters}, title = {Low-Complexity Precoding for Sum Rate Maximization in Downlink Massive MIMO Systems}, year = {2017}, volume = {6}, number = {2}, pages = {186-189}, doi = {10.1109/LWC.2017.2651069}, month = {April} }

Precoder Design for Signal Superposition in MIMO-NOMA Multicell Networks

Nguyen, Tuan, Duong, Poor & Shin
IEEE Journal on Selected Areas in Communications

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The throughput of users with poor channel conditions, such as those at a cell edge, is a bottleneck in wireless systems. A major part of the power budget must be allocated to serve these users in guaranteeing their quality-of-service (QoS) requirements, hampering QoS for other users, and thus compromising the system reliability. In non-orthogonal multiple access (NOMA), the message intended for a user with a poor channel condition is decoded by itself and by another user with a better channel condition. The message intended for the latter is then successively decoded by itself after canceling the interference of the former. The overall information throughput is thus improved by this particular successive decoding and interference cancellation. This paper aims to design linear precoders/beamformers for signal superposition at the base stations of NOMA multiple-input multiple-output multi-cellular systems to maximize the overall sum throughput subject to the users' QoS requirements, which are imposed independently on the users' channel conditions. This design problem is formulated as the maximization of a highly nonlinear and nonsmooth function subject to nonconvex constraints, which is very computationally challenging. Path-following algorithms for its solution, which invoke only a simple convex problem of moderate dimension at each iteration, are developed. Generating a sequence of improved points, these algorithms converge at least to a local optimum. Extensive numerical simulations are then provided to demonstrate their merit.

@article{7974739, author = {Van-Dinh Nguyen and Hoang Duong Tuan and Trung Q. Duong and H. Vincent Poor and Oh-Soon Shin}, journal = {IEEE Journal on Selected Areas in Communications}, title = {Precoder Design for Signal Superposition in MIMO-NOMA Multicell Networks}, year = {2017}, volume = {35}, number = {12}, pages = {2681-2695}, doi = {10.1109/JSAC.2017.2726007}, month = {Dec} }

Robust beamforming for secrecy rate in cooperative cognitive radio multicast communications

Nguyen, Duong, Shin, Nallanathan & Karagiannidis
2017 IEEE International Conference on Communications (ICC)

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In this paper, we propose a cooperative approach to improve the security of both primary and secondary systems in cognitive radio multicast communications. During their access to the frequency spectrum licensed to the primary users, the secondary unlicensed users assist the primary system in fortifying security by sending a jamming noise to the eavesdroppers, while simultaneously protect themselves from eavesdropping. The main objective of this work is to maximize the secrecy rate of the secondary system, while adhering to all individual primary users' secrecy rate constraints. In the case of passive eavesdroppers and imperfect channel state information knowledge at the transceivers, the utility function of interest is nonconcave and involved constraints are nonconvex, and thus, the optimal solutions are troublesome. To address this problem, we propose an iterative algorithm to arrive at a local optimum of the considered problem. The proposed iterative algorithm is guaranteed to achieve a Karush-Kuhn-Tucker solution.

@inproceedings{7996605, author = {Van-Dinh Nguyen and Trung Q. Duong and Oh-Soon Shin and Arumugam Nallanathan and George K. Karagiannidis}, booktitle = {2017 IEEE International Conference on Communications (ICC)}, title = {Robust beamforming for secrecy rate in cooperative cognitive radio multicast communications}, year = {2017}, pages = {1-6}, doi = {10.1109/ICC.2017.7996605}, month = {May} }

Spectral Efficiency of Full-Duplex Multi-user System: Beamforming Design, User Grouping, and Time Allocation

Nguyen, Nguyen, Nguyen & Shin
IEEE Access

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Full-duplex (FD) systems have emerged as an essential enabling technology to further increase the data rate of wireless communication systems. The key idea of FD is to serve multiple users over the same bandwidth with a base station (BS) that can simultaneously transmit and receive the signals. The most challenging issue in designing an FD system is to address both the harmful effects of residual self-interference caused by the transmit-to-receive antennas at the BS as well as the co-channel interference from an uplink user (ULU) to a downlink user (DLU). An efficient solution to these problems is to assign the ULUs/DLUs in different groups/slots, with each user served in multiple groups. Hence, this paper studies the joint design of transmit beamformers, ULUs/DLUs group assignment, and time allocation for each group. The specific aim is to maximize the sum rate under the ULU/DLU minimum throughput constraints. The utility function of interest is a difficult nonconcave problem, and the involved constraints are also nonconvex, and so this is a computationally troublesome problem. To solve this optimization problem, we propose a new path-following algorithm for computational solutions to arrive at least the local optima. Each iteration involves only a simple convex quadratic program. We prove that the proposed algorithm iteratively improves the objective while guaranteeing convergence. Simulation results confirm the fast convergence of the proposed algorithm with substantial performance improvements over existing approaches.

@article{7866857, author = {Van-Dinh Nguyen and Hieu V. Nguyen and Chuyen T. Nguyen and Oh-Soon Shin}, journal = {IEEE Access}, title = {Spectral Efficiency of Full-Duplex Multi-user System: Beamforming Design, User Grouping, and Time Allocation}, year = {2017}, volume = {5}, pages = {5785-5797}, doi = {10.1109/ACCESS.2017.2668384} }

Spectral and Energy Efficiencies in Full-Duplex Wireless Information and Power Transfer

Nguyen, Duong, Tuan, Shin & Poor
IEEE Transactions on Communications

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A communication system is considered consisting of a full-duplex multiple-antenna base station (BS) and multiple single-antenna downlink users (DLUs) and single-antenna uplink users (ULUs), where the latter need to harvest energy for transmitting information to the BS. The communication is thus divided into two phases. In the first phase, the BS uses all available antennas for conveying information to DLUs and wireless energy to ULUs via information and energy beamforming, respectively. In the second phase, ULUs send their independent information to the BS using their harvested energy while the BS transmits the information to the DLUs. In both the phases, the communication is operated at the same time and over the same frequency band. The aim is to maximize the sum rate and energy efficiency under ULU achievable information throughput constraints by jointly optimizing beamforming and time allocation. The utility functions of interest are nonconcave and the involved constraints are nonconvex, so these problems are computationally troublesome. To address them, path-following algorithms are proposed to arrive at least at local optima. The proposed algorithms iteratively improve the objectives with convergence guaranteed. Simulation results demonstrate that they achieve rapid convergence and outperform conventional solutions.

@article{7845589, author = {Van-Dinh Nguyen and Trung Q. Duong and Hoang Duong Tuan and Oh-Soon Shin and H. Vincent Poor}, journal = {IEEE Transactions on Communications}, title = {Spectral and Energy Efficiencies in Full-Duplex Wireless Information and Power Transfer}, year = {2017}, volume = {65}, number = {5}, pages = {2220-2233}, doi = {10.1109/TCOMM.2017.2665488}, month = {May} }

Sum Rate Maximization Based on Sub-Array Antenna Selection in a Full-Duplex System

Nguyen, Nguyen, Dobre & Shin
GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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This paper considers a full-duplex system for a base station (BS) serving both uplink and downlink users simultaneously on the same frequency. A new design of the selection of the half-array antenna mode at the BS (transmit or receive mode) over the time phases and user assignments is proposed and its beamforming design and power allocation problem are optimized under the effect of both residual self- interference and co-channel interference. The aim is to maximize the overall sum rate subject to the users' quality of service requirements, which is formulated as a highly nonlinear function subject to non-convex constraints. To solve this non-convex problem, we propose an iterative low- complexity algorithm. Simulation results demonstrate that the proposed algorithm provides a fast convergence and substantially outperforms all existing schemes.

@inproceedings{8254806, author = {Hieu V. Nguyen and Van-Dinh Nguyen and Octavia A. Dobre and Oh-Soon Shin}, booktitle = {GLOBECOM 2017 - 2017 IEEE Global Communications Conference}, title = {Sum Rate Maximization Based on Sub-Array Antenna Selection in a Full-Duplex System}, year = {2017}, pages = {1-6}, doi = {10.1109/GLOCOM.2017.8254806}, month = {Dec} }

2016

An Efficient Zero-Forcing Precoding Design for Cognitive MIMO Broadcast Channels

Nguyen, Nguyen & Shin
IEEE Communications Letters

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We consider linear precoding design for an underlay cognitive radio multiple-input multiple-output broadcast channel in the presence of multiple primary users (PUs). Under the assumption of imperfect channel state information (CSI) of the PUs, the objective of this letter is to maximize the sum rate of the secondary system, subject to the power budget at the secondary base station and the interference power constraints at the PUs. The design problem is non-convex, and thus is difficult to solve in general. Herein, we first convert a non-convex constraint related to the imperfect CSI of the PUs into a convex constraint, and then invoke a rank relaxation method to transform the considered problem into a convex-concave problem based on a downlink-uplink duality result. Simulation results are provided to demonstrate the effectiveness and robustness of the proposed design against CSI imperfection.

@article{7484669, author = {Van-Dinh Nguyen and Hieu V. Nguyen and Oh-Soon Shin}, journal = {IEEE Communications Letters}, title = {An Efficient Zero-Forcing Precoding Design for Cognitive MIMO Broadcast Channels}, year = {2016}, volume = {20}, number = {8}, pages = {1575-1578}, doi = {10.1109/LCOMM.2016.2576445}, month = {Aug} }

Joint Information and Jamming Beamforming for Secrecy Rate Maximization in Cognitive Radio Networks

Nguyen, Duong, Dobre & Shin
IEEE Transactions on Information Forensics and Security

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In this paper, we consider the secure beamforming design for an underlay cognitive radio multiple-input single-output broadcast channel in the presence of multiple passive eavesdroppers. Our goal is to design a jamming noise (JN) transmit strategy to maximize the secrecy rate of the secondary system. By utilizing the zero-forcing method to eliminate the interference caused by JN to the secondary user, we study the joint optimization of the information and JN beamforming for secrecy rate maximization of the secondary system while satisfying all the interference power constraints at the primary users, as well as the per-antenna power constraint at the secondary transmitter. For an optimal beamforming design, the original problem is a nonconvex program, which can be reformulated as a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and propose a barrier interior-point method to solve the resulting saddle point problem based on a duality result. To find the global optimal solution, we transform the considered problem into an unconstrained optimization problem. We then employ Broyden-Fletcher-Goldfarb-Shanno method to solve the resulting unconstrained problem, which helps reduce the complexity significantly, compared with the conventional methods. Simulation results show the fast convergence of the proposed algorithm and substantial performance improvements over the existing approaches.

@article{7523442, author = {Van-Dinh Nguyen and Trung Q. Duong and Octavia A. Dobre and Oh-Soon Shin}, journal = {IEEE Transactions on Information Forensics and Security}, title = {Joint Information and Jamming Beamforming for Secrecy Rate Maximization in Cognitive Radio Networks}, year = {2016}, volume = {11}, number = {11}, pages = {2609-2623}, doi = {10.1109/TIFS.2016.2594131}, month = {Nov} }

Secrecy rate maximization in a cognitive radio network with artificial noise aided for MISO multi-eves

Nguyen, Duong, Dobre & Shin
2016 IEEE International Conference on Communications (ICC)

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In this paper, we consider beamforming design for an underlay cognitive radio multiple-input single-output broadcast channel, where a pair of secondary users coexists with multiple primary receivers. There exist multiple malicious eavesdroppers who attempt to overhear the confidential messages from the secondary system. When the channel state information of the eavesdroppers can only be obtained in the statistical sense, we transform the constraint which results from the statistical information of the passive eavesdroppers into a linear matrix inequality and convex constraint. To improve the secrecy rate of the secondary system, we aim to design a jamming noise to degrade the eavesdroppers. The main objective of the design is to maximize the secrecy rate of the secondary system while satisfying all the interference power constraints at the primary users and per-antenna power constraint at the secondary transmitter. The original problem is a nonconvex program, which can be reformulated to a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and it can be efficiently solved. Moreover, to develop an efficient resource allocation scheme we transform the relaxed problem into an equivalent problem based on a duality result.

@inproceedings{7511333, author = {Van-Dinh Nguyen and Trung Q. Duong and Octavia A. Dobre and Oh-Soon Shin}, booktitle = {2016 IEEE International Conference on Communications (ICC)}, title = {Secrecy rate maximization in a cognitive radio network with artificial noise aided for MISO multi-eves}, year = {2016}, pages = {1-6}, doi = {10.1109/ICC.2016.7511333}, month = {May} }

Secure Full-Duplex Cognitive Relay Networks with Optimal Relay Selection Scheme

Nguyen, Kundu, Nguyen, Duong & Fan
2016 IEEE Global Communications Conference (GLOBECOM)

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In this paper, we investigate the secure communication of cognitive full-duplex relay networks in the presence of multiple eavesdroppers and multiple primary receivers. In the considered network, multiple full-duplex relays are deployed to transfer information in the secondary network, under the malicious attempts of non- colluding/colluding eavesdroppers. Meanwhile, the transmit powers of secondary transmitters are constrained by the quality-of-service of the primary network. The optimal relay selection scheme is proposed to enhance the secrecy performance of the considered system. We study the secrecy performance by providing the exact closed- form and asymptotic expressions of the proposed system secrecy outage probability. We have demonstrated that increasing the number of full- duplex relays can improve the security performance. At the illegitimate side, using colluding eavesdroppers and increasing the number of eavesdroppers put information confidentiality at a greater risk. Besides, the transmit power and the desired outage probability of the primary network have great influences on the secrecy outage probability of the secondary network.

@inproceedings{7842251, author = {Nam-Phong Nguyen and Chinmoy Kundu and Van-Dinh Nguyen and Trung Q. Duong and Lisheng Fan}, booktitle = {2016 IEEE Global Communications Conference (GLOBECOM)}, title = {Secure Full-Duplex Cognitive Relay Networks with Optimal Relay Selection Scheme}, year = {2016}, pages = {1-6}, doi = {10.1109/GLOCOM.2016.7842251}, month = {Dec} }

Sum rate maximization for full duplex wireless-powered communication networks

Nguyen, Nguyen, Kang, Kim & Shin
2016 24th European Signal Processing Conference (EUSIPCO)

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We consider a full duplex multiuser multiple-input multiple-output system and study the sum rate maximization in a wireless powered communication networks. We assume that the users of uplink (UL) channel have no available power supply and thus a harvest-then-transmit protocol is utilized. Specifically, the base station (BS) first conveys simultaneously the energy to all UL users via energy beamforming and also transmit the information to all users in the downlink (DL) channel via information beamforming. Then, the users in the UL channel send their independent information to the BS using their harvested energy in the second phase. Since the utility function of the sum rate maximization is nonconvex, and thus, the optimal solution is difficult to find in general. To solve this problem, we propose an iterative algorithm to obtain suboptimal solution based on semidefinite program in each iteration. Simulation results demonstrate that the proposed design outperforms the conventional design.

@inproceedings{7760358, author = {Van-Dinh Nguyen and Hieu V. Nguyen and Gil-Mo Kang and Hyeon Min Kim and Oh-Soon Shin}, booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)}, title = {Sum rate maximization for full duplex wireless-powered communication networks}, year = {2016}, pages = {798-802}, doi = {10.1109/EUSIPCO.2016.7760358}, month = {Aug} }

2015

Channel estimation based on time-shifted pilots in multicell massive MIMO systems

Nguyen, Nguyen & Shin
2015 International Conference on Communications, Management and Telecommunications (ComManTel)

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Massive multiple-input multiple-output (MIMO) is a promising technique for future wireless communication systems. The principal benefits of using massive MIMO systems include the increase in the uplink achievable rate when the number of antennas at the base station (BS) increases. However, pilot contamination is a significant obstacle to obtaining the benefits of massive MIMO system. Specifically, the achievable rate affected by pilot contamination will saturate to a certain point even when the number of antennas increases. Recendy, the concept of time-shifted pilots was proposed. According to the idea, uplink training sequences from the users in a certain cell are transmitted to their BS during the downlink period of other cells. In this paper, we propose two uplink training strategies by combining the time-shifted pilots and the conventional time-aligned pilots to improve the performance of channel estimation in massive MIMO systems. Furthermore, we also derive a lower bound of the uplink achievable rate for the case where the number of antennas at the BS is finite. Numerical results are presented to verify the analysis.

@inproceedings{7394265, author = {Van-Dinh Nguyen and Hien V. Nguyen and Oh-Soon Shin}, booktitle = {2015 International Conference on Communications, Management and Telecommunications (ComManTel)}, title = {Channel estimation based on time-shifted pilots in multicell massive MIMO systems}, year = {2015}, pages = {83-87}, doi = {10.1109/ComManTel.2015.7394265}, month = {Dec} }

Energy harvesting for cooperative wireless sensor networks with a nonlinear power consumption model

Nguyen & Shin
2015 International Conference on Information and Communication Technology Convergence (ICTC)

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We consider an energy harvesting for cooperative wireless sensor networks with a nonlinear power consumption model. The information transmission from the source to the destination is assumed to occur via several clusters of decode- and-forward relays. All the source and relay nodes are assumed to have the ability to harvest energy from the environment and use the harvested energy to transmit the information to the next hop. We establish an optimization problem that maximizes the total throughput of the end-to-end link over a number of transmission blocks, subject to constraints of energy causality, battery overflow and the duration of energy harvesting. The optimization problem is found to be a non-concave program and we present a simple algorithm to solve the non-concave program. Numerical results are provided to evaluate the performance of the proposed optimization.

@inproceedings{7354527, author = {Van-Dinh Nguyen and Oh-Soon Shin}, booktitle = {2015 International Conference on Information and Communication Technology Convergence (ICTC)}, title = {Energy harvesting for cooperative wireless sensor networks with a nonlinear power consumption model}, year = {2015}, pages = {197-199}, doi = {10.1109/ICTC.2015.7354527}, month = {Oct} }

Opportunistic relaying with wireless energy harvesting in a cognitive radio system

Nguyen, Dinh-Van & Shin
2015 IEEE Wireless Communications and Networking Conference (WCNC)

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In this paper, the performance of opportunistic relay selection (ORS) in a cognitive radio is analyzed over flat Rayleigh fading channels. Data transmission between source and destination is assumed to be entirely performed via the relays. Relay nodes are assumed to have ability to harvest energy from the source signal and use that harvested energy to forward the information to the destination. Specifically, we derive an exact expression for the outage probability of the secondary system considering the maximum transmit power at the secondary transmitter and relays, energy harvesting efficiency at relays, and interference constraint at the primary receiver. Under the assumption of perfect channel state information at the receivers, we evaluate the outage probability of a cognitive radio system with ORS and energy harvesting.

@inproceedings{7127450, author = {Van-Dinh Nguyen and Son Dinh-Van and Oh-Soon Shin}, booktitle = {2015 IEEE Wireless Communications and Networking Conference (WCNC)}, title = {Opportunistic relaying with wireless energy harvesting in a cognitive radio system}, year = {2015}, pages = {87-92}, doi = {10.1109/WCNC.2015.7127450}, month = {March} }

Physical Layer Security for Primary System: A Symbiotic Approach in Cooperative Cognitive Radio Networks

Nguyen, Duong & Shin
2015 IEEE Global Communications Conference (GLOBECOM)

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In this paper, we proposed a symbiotic approach for a secure primary network by allowing the secondary users to send the jamming noise to degrade the wiretap ability of the eavesdropper. In particular, assuming that the global channel state information is perfectly known at tranceivers, we consider the case of the primary transmitter equipped with only one antenna, which implies that the primary transmitter does not have beamforming capability. As the reward of having access to the frequency spectrum which is licensed by the the primary user, the secondary transmitter will assist the primary systems in terms of security by sending the jamming noise to the eavesdropper. We propose an algorithm to find the optimal transmit power for maximizing the secrecy capacity of the primary system. Numerical results are presented to validate our proposed scheme.

@inproceedings{7417101, author = {Van-Dinh Nguyen and Trung Q. Duong and Oh-Soon Shin}, booktitle = {2015 IEEE Global Communications Conference (GLOBECOM)}, title = {Physical Layer Security for Primary System: A Symbiotic Approach in Cooperative Cognitive Radio Networks}, year = {2015}, pages = {1-6}, doi = {10.1109/GLOCOM.2015.7417101}, month = {Dec} }

Secrecy Capacity of the Primary System in a Cognitive Radio Network

Nguyen, Hoang & Shin
IEEE Transactions on Vehicular Technology

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With fast growth of wireless services, secrecy has become an increasingly important issue for wireless networks. In this paper, we investigate the secrecy capacity of the primary system in a cognitive radio system based on artificial noise, which has been proposed for dealing with the eavesdropper. We first consider a special case of one eavesdropper and two regimes of the eavesdropping channel condition. Specifically, we analyze the impact of interference generated by a secondary system toward the primary system in a cognitive radio system. The channel state information (CSI) of the primary channel is assumed to be perfectly known at both the primary transmitter and receiver, whereas that of the eavesdropper is partially known. Under these assumptions, we derive analytical expressions for the ergodic secrecy capacity in the cases of strong eavesdropping channel and weak eavesdropping channel and analyze the impact of the secondary system on the primary ergodic secrecy capacity. Moreover, we extend the analysis to the general case of arbitrary eavesdropping channel condition and arbitrary number of eavesdroppers. Some numerical results will be also presented to verify the analysis.

@article{6905826, author = {Van-Dinh Nguyen and Tiep M. Hoang and Oh-Soon Shin}, journal = {IEEE Transactions on Vehicular Technology}, title = {Secrecy Capacity of the Primary System in a Cognitive Radio Network}, year = {2015}, volume = {64}, number = {8}, pages = {3834-3843}, doi = {10.1109/TVT.2014.2359246}, month = {Aug} }

2014

The problem of pilot contamination reduction in massive MIMO for physical channel models

Nguyen, Dinh-Van, Shin, Lee & Shin
2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN)

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In this paper, we consider massive multiple-input multiple-output (MIMO) multicell systems with very large antenna arrays at base station (BS). We propose an eigenvalue decomposition (EVD) approach to evaluate the covariance matrix of the received vector at the BS, and focus on a physical channel model where the angular domain is partitioned into a finite number of directions, so-called angle of arrival (AOA). In this scheme, the asymptotic orthogonality of fast fading coefficients between users and the BSs would be true only when the number of BS antennas goes to infinity. Furthermore, we derive an optimal multiuser detector by taking the channel estimation and the intercell interference from other cells into account to decrease channel estimation error, hence, the system performance will be enhanced significantly, even in strong inter-cell interference environment. Numerical results are provided to verify our analysis.

@inproceedings{6876817, author = {Van-Dinh Nguyen and Son Dinh-Van and Yoan Shin and Won Cheol Lee and Oh-Soon Shin}, booktitle = {2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN)}, title = {The problem of pilot contamination reduction in massive MIMO for physical channel models}, year = {2014}, pages = {378-382}, doi = {10.1109/ICUFN.2014.6876817}, month = {July} }

Physical layer security for cognitive radio networks

null Van-Dinh Nguyen, Duong & Shin
Trusted Communications with Physical Layer Security for 5G and Beyond

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In this chapter, we have presented physical layer (PHY)-security of cognitive radio network (CRN). Specifically, we have described fundamental PHY-security in CRN and pointed out some recent enhanced protocols available in the literature. Typical applications of artificial noise for the primary system, secondary system, and cooperative CRN have been investigated. We have also presented a powerful technique such as beamforming design on resource allocation problems for such schemes. For the primary system, based on the derived capacity formula, the impact of the secondary system on the secrecy capacity is analysed. In particular, we point out that when the eavesdropper is very far from the primary system, the use of artificial noise is not effective to protect the primary system from eavesdropping. For the secondary system, the proposed approach offers a better performance and is quite robust when compared to the existing approaches. In addition, a cooperative CRN is also presented to improve PHY-security of the primary system. Simulation results are shown to verify the theoretical development.

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Summarizing Proof-of-Stake mechanisms and their practical deployments: applications, attacks, solutions, and future directions

Nguyen, Hoang, Nguyen, Nguyen & Dutkiewicz
Proof-of-Stake for Blockchain Networks

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In this chapter, we have first provided a concise summary of the PoS consensus mechanism, covering its key concepts and benefits. Then, we have presented notable advanced PoS mechanisms and their real-world applications. Moreover, we have reviewed PoS security properties and introduced potential attacks targeting PoS networks as well as potential approaches to mitigate them, including double-spending, Sybil, race, bribery, transaction denial, grinding, desynchronization, eclipse, long-range, nothing-at-stakes, past majority, and 51\% attacks. Furthermore, we discussed the challenges faced in PoS networks and potential research directions, including stake pool formation, federated blockchain systems, and PoS shardings.

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Nguyen, N. T., Nguyen, V.-D., Nguyen, H. V., Ngo, H. Q., Chatzinotas, S., & Juntti, M. (2022). Downlink throughput of cell-free massive MIMO systems assisted by hybrid relay-reflecting intelligent surfaces. ICC 2022 - IEEE International Conference on Communications, 1475–1480. https://doi.org/10.1109/ICC45855.2022.9838672

Nguyen, N. T., Nguyen, V.-D., Nguyen, H. V., Ngo, H. Q., Chatzinotas, S., & Juntti, M. (2023). Spectral efficiency analysis of hybrid relay-reflecting intelligent surface-assisted cell-free massive MIMO systems. IEEE Transactions on Wireless Communications, 22(5), 3397–3416. https://doi.org/10.1109/TWC.2022.3217828

Nguyen, N. T., Nguyen, V.-D., Nguyen, H. V., Ngo, H. Q., Swindlehurst, A. L., & Juntti, M. (2025). Performance analysis and power allocation for massive MIMO ISAC systems. IEEE Transactions on Signal Processing, 73, 1691–1707. https://doi.org/10.1109/TSP.2025.3554012

Nguyen, N. T., Nguyen, V.-D., Van Nguyen, H., Wu, Q., Tölli, A., Chatzinotas, S., & Juntti, M. (2024). Fairness enhancement of UAV systems with hybrid active-passive RIS. IEEE Transactions on Wireless Communications, 23(5), 4379–4396. https://doi.org/10.1109/TWC.2023.3317934

Nguyen, N. T., Nguyen, V.-D., Wu, Q., Tölli, A., Chatzinotas, S., & Juntti, M. (2022a). Hybrid active-passive reconfigurable intelligent surface-assisted multi-user MISO systems. 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), 1–5. https://doi.org/10.1109/SPAWC51304.2022.9833956

Nguyen, N. T., Nguyen, V.-D., Wu, Q., Tölli, A., Chatzinotas, S., & Juntti, M. (2022b). Hybrid active-passive reconfigurable intelligent surface-assisted UAV communications. GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 3126–3131. https://doi.org/10.1109/GLOBECOM48099.2022.10001719

Nguyen, N. T., Shlezinger, N., Ngo, K.-H., Nguyen, V.-D., & Juntti, M. (2023). Joint communications and sensing design for multi-carrier MIMO systems. 2023 IEEE Statistical Signal Processing Workshop (SSP), 110–114. https://doi.org/10.1109/SSP53291.2023.10207952

Nguyen, N.-P., Kundu, C., Nguyen, V.-D., Duong, T. Q., & Fan, L. (2016). Secure full-duplex cognitive relay networks with optimal relay selection scheme. 2016 IEEE Global Communications Conference (GLOBECOM), 1–6. https://doi.org/10.1109/GLOCOM.2016.7842251

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Nguyen, P. X., Nguyen, V.-D., Nguyen, H. V., & Shin, O.-S. (2021b). UAV-assisted secure communications in terrestrial cognitive radio networks: Joint power control and 3D trajectory optimization. IEEE Transactions on Vehicular Technology, 70(4), 3298–3313. https://doi.org/10.1109/TVT.2021.3062283

Nguyen, T.-T., Nguyen, V.-D., Lee, J.-H., & Kim, Y.-H. (2019). Sum rate maximization for multi-user wireless powered IoT network with non-linear energy harvester: Time and power allocation. IEEE Access, 7, 149698–149710. https://doi.org/10.1109/ACCESS.2019.2947321

Nguyen, T.-T., Nguyen, V.-D., Pham, Q.-V., Lee, J.-H., & Kim, Y.-H. (2021). Resource allocation for AF relaying wireless-powered networks with nonlinear energy harvester. IEEE Communications Letters, 25(1), 229–233. https://doi.org/10.1109/LCOMM.2020.3023937

Nguyen, T.-T., Pham, Q.-V., Nguyen, V.-D., Lee, J.-H., & Kim, Y.-H. (2020). Resource allocation for energy efficiency in OFDMA-enabled WPCN. IEEE Wireless Communications Letters, 9(12), 2049–2053. https://doi.org/10.1109/LWC.2020.3012206

Nguyen, T.-V., Huynh-The, T., Nguyen, V.-D., Da Costa, D. B., Qingyang Hu, R., & An, B. (2022). An efficient deep CNN design for EH short-packet communications in multihop cognitive IoT networks. ICC 2022 - IEEE International Conference on Communications, 2102–2107. https://doi.org/10.1109/ICC45855.2022.9839014

Nguyen, T.-V., Nguyen, V.-D., Costa, D. B. da, & An, B. (2020). Hybrid user pairing for spectral and energy efficiencies in multiuser MISO-NOMA networks with SWIPT. IEEE Transactions on Communications, 68(8), 4874–4890. https://doi.org/10.1109/TCOMM.2020.2994204

Nguyen, T.-V., Nguyen, V.-D., Costa, D. B. da, & An, B. (2021). Short-packet communications in multi-hop WPINs: Performance analysis and deep learning design. 2021 IEEE Global Communications Conference (GLOBECOM), 1–6. https://doi.org/10.1109/GLOBECOM46510.2021.9685765

Nguyen, T.-V., Nguyen, V.-D., Costa, D. B. da, Huynh-The, T., Hu, R. Q., & An, B. (2023). Short-packet communications in multihop networks with WET: Performance analysis and deep learning-aided optimization. IEEE Transactions on Wireless Communications, 22(1), 439–456. https://doi.org/10.1109/TWC.2022.3195234

Nguyen, T.-V., Nguyen, V.-D., Do, T.-N., Costa, D. B. da, & An, B. (2019). Spectral efficiency maximization for multiuser MISO-NOMA downlink systems with SWIPT. 2019 IEEE Global Communications Conference (GLOBECOM), 1–6. https://doi.org/10.1109/GLOBECOM38437.2019.9014339

Nguyen, V.-D., Chatzinotas, S., Ottersten, B., & Duong, T. Q. (2022). FedFog: Network-aware optimization of federated learning over wireless fog-cloud systems. IEEE Transactions on Wireless Communications, 21(10), 8581–8599. https://doi.org/10.1109/TWC.2022.3167263

Nguyen, V.-D., Dinh-Van, S., & Shin, O.-S. (2015). Opportunistic relaying with wireless energy harvesting in a cognitive radio system. 2015 IEEE Wireless Communications and Networking Conference (WCNC), 87–92. https://doi.org/10.1109/WCNC.2015.7127450

Nguyen, V.-D., Dinh-Van, S., Shin, Y., Lee, W. C., & Shin, O.-S. (2014). The problem of pilot contamination reduction in massive MIMO for physical channel models. 2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN), 378–382. https://doi.org/10.1109/ICUFN.2014.6876817

Nguyen, V.-D., Duong, T. Q., Dobre, O. A., & Shin, O.-S. (2016a). Joint information and jamming beamforming for secrecy rate maximization in cognitive radio networks. IEEE Transactions on Information Forensics and Security, 11(11), 2609–2623. https://doi.org/10.1109/TIFS.2016.2594131

Nguyen, V.-D., Duong, T. Q., Dobre, O. A., & Shin, O.-S. (2016b). Secrecy rate maximization in a cognitive radio network with artificial noise aided for MISO multi-eves. 2016 IEEE International Conference on Communications (ICC), 1–6. https://doi.org/10.1109/ICC.2016.7511333

Nguyen, V.-D., Duong, T. Q., & Shin, O.-S. (2015). Physical layer security for primary system: A symbiotic approach in cooperative cognitive radio networks. 2015 IEEE Global Communications Conference (GLOBECOM), 1–6. https://doi.org/10.1109/GLOCOM.2015.7417101

Nguyen, V.-D., Duong, T. Q., Shin, O.-S., Nallanathan, A., & Karagiannidis, G. K. (2017a). Enhancing PHY security of cooperative cognitive radio multicast communications. IEEE Transactions on Cognitive Communications and Networking, 3(4), 599–613. https://doi.org/10.1109/TCCN.2017.2748132

Nguyen, V.-D., Duong, T. Q., Shin, O.-S., Nallanathan, A., & Karagiannidis, G. K. (2017b). Robust beamforming for secrecy rate in cooperative cognitive radio multicast communications. 2017 IEEE International Conference on Communications (ICC), 1–6. https://doi.org/10.1109/ICC.2017.7996605

Nguyen, V.-D., Duong, T. Q., Tuan, H. D., Shin, O.-S., & Poor, H. V. (2017). Spectral and energy efficiencies in full-duplex wireless information and power transfer. IEEE Transactions on Communications, 65(5), 2220–2233. https://doi.org/10.1109/TCOMM.2017.2665488

Nguyen, V.-D., Hoang, T. M., & Shin, O.-S. (2015). Secrecy capacity of the primary system in a cognitive radio network. IEEE Transactions on Vehicular Technology, 64(8), 3834–3843. https://doi.org/10.1109/TVT.2014.2359246

Nguyen, V.-D., Nguyen, C. T., Nguyen, H. V., & Shin, O.-S. (2017a). Joint beamforming and antenna selection for sum rate maximization in cognitive radio networks. IEEE Communications Letters, 21(6), 1369–1372. https://doi.org/10.1109/LCOMM.2017.2679186

Nguyen, V.-D., Nguyen, H. V., Dobre, O. A., & Shin, O.-S. (2018a). A new design paradigm for secure full-duplex multiuser systems. IEEE Journal on Selected Areas in Communications, 36(7), 1480–1498. https://doi.org/10.1109/JSAC.2018.2824379

Nguyen, V.-D., Nguyen, H. V., Dobre, O. A., & Shin, O.-S. (2018b). On the design of secure full-duplex multiuser systems under user grouping method. 2018 IEEE International Conference on Communications (ICC), 1–6. https://doi.org/10.1109/ICC.2018.8422451

Nguyen, V.-D., Nguyen, H. V., Kang, G.-M., Kim, H. M., & Shin, O.-S. (2016). Sum rate maximization for full duplex wireless-powered communication networks. 2016 24th European Signal Processing Conference (EUSIPCO), 798–802. https://doi.org/10.1109/EUSIPCO.2016.7760358

Nguyen, V.-D., Nguyen, H. V., Nguyen, C. T., & Shin, O.-S. (2017b). Spectral efficiency of full-duplex multi-user system: Beamforming design, user grouping, and time allocation. IEEE Access, 5, 5785–5797. https://doi.org/10.1109/ACCESS.2017.2668384

Nguyen, V.-D., Nguyen, H. V., & Shin, O.-S. (2015). Channel estimation based on time-shifted pilots in multicell massive MIMO systems. 2015 International Conference on Communications, Management and Telecommunications (ComManTel), 83–87. https://doi.org/10.1109/ComManTel.2015.7394265

Nguyen, V.-D., Nguyen, H. V., & Shin, O.-S. (2016). An efficient zero-forcing precoding design for cognitive MIMO broadcast channels. IEEE Communications Letters, 20(8), 1575–1578. https://doi.org/10.1109/LCOMM.2016.2576445

Nguyen, V.-D., Sharma, S. K., Vu, T. X., Chatzinotas, S., & Ottersten, B. (2021). Efficient federated learning algorithm for resource allocation in wireless IoT networks. IEEE Internet of Things Journal, 8(5), 3394–3409. https://doi.org/10.1109/JIOT.2020.3022534

Nguyen, V.-D., & Shin, O.-S. (2015). Energy harvesting for cooperative wireless sensor networks with a nonlinear power consumption model. 2015 International Conference on Information and Communication Technology Convergence (ICTC), 197–199. https://doi.org/10.1109/ICTC.2015.7354527

Nguyen, V.-D., & Shin, O.-S. (2018). Cooperative prediction-and-sensing-based spectrum sharing in cognitive radio networks. IEEE Transactions on Cognitive Communications and Networking, 4(1), 108–120. https://doi.org/10.1109/TCCN.2017.2776138

Nguyen, V.-D., & Shin, O.-S. (2019). An efficient design for NOMA-assisted MISO-SWIPT systems with AC computing. IEEE Access, 7, 97094–97105. https://doi.org/10.1109/ACCESS.2019.2928877

Nguyen, V.-D., Tran, L.-N., Duong, T. Q., Shin, O.-S., & Farrell, R. (2017). An efficient precoder design for multiuser MIMO cognitive radio networks with interference constraints. IEEE Transactions on Vehicular Technology, 66(5), 3991–4004. https://doi.org/10.1109/TVT.2016.2602844

Nguyen, V.-D., Tuan, H. D., Duong, T. Q., Poor, H. V., & Shin, O.-S. (2017a). Convex quadratic programming for maximizing sum throughput in MIMO-NOMA multicell networks. GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 1–6. https://doi.org/10.1109/GLOCOM.2017.8254177

Nguyen, V.-D., Tuan, H. D., Duong, T. Q., Poor, H. V., & Shin, O.-S. (2017b). Precoder design for signal superposition in MIMO-NOMA multicell networks. IEEE Journal on Selected Areas in Communications, 35(12), 2681–2695. https://doi.org/10.1109/JSAC.2017.2726007

Nguyen, V.-D., Tuan, H. D., Duong, T. Q., Shin, O.-S., & Poor, H. V. (2017). Joint fractional time allocation and beamforming for downlink multiuser MISO systems. IEEE Communications Letters, 21(12), 2650–2653. https://doi.org/10.1109/LCOMM.2017.2747544

Nguyen, V.-D., Vu, T. X., Nguyen, N. T., Nguyen, D. C., Juntti, M., Luong, N. C., Hoang, D. T., Nguyen, D. N., & Chatzinotas, S. (2023). Enabling intelligent traffic steering in a hierarchical open radio access network. GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 5232–5237. https://doi.org/10.1109/GLOBECOM54140.2023.10437249

Nguyen, V.-D., Vu, T. X., Nguyen, N. T., Nguyen, D. C., Juntti, M., Luong, N. C., Hoang, D. T., Nguyen, D. N., & Chatzinotas, S. (2024). Network-aided intelligent traffic steering in 6G o-RAN: A multi-layer optimization framework. IEEE Journal on Selected Areas in Communications, 42(2), 389–405. https://doi.org/10.1109/JSAC.2023.3336183

Nhat Le, Q., Nguyen, V.-D., Dobre, O. A., & Shin, H. (2023). RIS-assisted full-duplex integrated sensing and communication. IEEE Wireless Communications Letters, 12(10), 1677–1681. https://doi.org/10.1109/LWC.2023.3285391

Papazafeiropoulos, A., Pan, C., Elbir, A. M., Nguyen, V.-D., Kourtessis, P., & Chatzinotas, S. (2023). Asymptotic analysis of max-min weighted SINR for IRS-assisted MISO systems with hardware impairments. IEEE Wireless Communications Letters, 12(2), 192–196. https://doi.org/10.1109/LWC.2021.3095678

Quan, M. K., Nguyen, D. C., Nguyen, V.-D., Wijayasundara, M., Setunge, S., & Pathirana, P. N. (2024). Toward privacy-preserving waste classification in the internet of things. IEEE Internet of Things Journal, 11(14), 24814–24830. https://doi.org/10.1109/JIOT.2024.3386727

Sang, N. H., Hai, N. D., Anh, N. D. D., Cong Luong, N., Nguyen, V.-D., Gong, S., Niyato, D., & In Kim, D. (2025). Wireless power transfer meets semantic communication for resource-constrained IoT networks: A joint transmission mode selection and resource management approach. IEEE Internet of Things Journal, 12(1), 556–568. https://doi.org/10.1109/JIOT.2024.3464646

Shaon, S., Nguyen, V.-D., & Nguyen, D. C. (2025). Latency optimization for wireless federated learning in multihop networks. IEEE Transactions on Vehicular Technology, 74(11), 18318–18323. https://doi.org/10.1109/TVT.2025.3577530

Tan, H. N. L., Nguyen, V.-D., Huynh-The, T., Nguyen, T.-V., & Vo, P. L. (2024). Security improvement for deep learning-based semantic communication systems. 2024 International Conference on Advanced Technologies for Communications (ATC), 717–721. https://doi.org/10.1109/ATC63255.2024.10908341

Thanh Van, N. T., Luong, N. C., Feng, S., Nguyen, V.-D., & Kim, D. I. (2023). Evolutionary games for dynamic network resource selection in RSMA-enabled 6G networks. IEEE Journal on Selected Areas in Communications, 41(5), 1320–1335. https://doi.org/10.1109/JSAC.2023.3240779

Tran, D.-H., Nguyen, V.-D., Chatzinotas, S., Vu, T. X., & Ottersten, B. (2022). UAV relay-assisted emergency communications in IoT networks: Resource allocation and trajectory optimization. IEEE Transactions on Wireless Communications, 21(3), 1621–1637. https://doi.org/10.1109/TWC.2021.3105821

Tran, D.-H., Nguyen, V.-D., Gautam, S., Chatzinotas, S., Vu, T. X., & Ottersten, B. (2020). Resource allocation for UAV relay-assisted IoT communication networks. 2020 IEEE Globecom Workshops (GC Wkshps, 1–7. https://doi.org/10.1109/GCWkshps50303.2020.9367522

Truong, T.-V., Luu, Q.-T., & Nguyen, V.-D. (2024). Efficient resource allocation framework for network slicing-enabled open RAN. 2024 Tenth International Conference on Communications and Electronics (ICCE), 747–752. https://doi.org/10.1109/ICCE62051.2024.10634735

Truong, T.-V., Nguyen, V.-D., Luu, Q.-T., Vo, P.-S., Nguyen, X.-P., Kavehmadavani, F., & Chatzinotas, S. (2026). Accelerating resource allocation in open RAN slicing via deep reinforcement learning. IEEE Transactions on Network and Service Management, 23, 3055–3070. https://doi.org/10.1109/TNSM.2026.3665553

Tuan, B. M., Nguyen, V.-D., Nguyen, D. N., Trung, N. L., Huynh, N. V., Hoang, D. T., Krunz, M., & Dutkiewicz, E. (2026). Deep learning-driven friendly jamming for secure multicarrier ISAC under channel uncertainty. IEEE Transactions on Communications, 1–1. https://doi.org/10.1109/TCOMM.2026.3675471

Van Chien, T., Minh Quan, N., Shin, O.-S., & Nguyen, V.-D. (2025). Metaheuristic optimization of trajectory and dynamic time splitting for UAV communication systems. IEEE Communications Letters, 29(6), 1200–1204. https://doi.org/10.1109/LCOMM.2025.3556714

Van Huynh, D., Nguyen, V.-D., Chatzinotas, S., Khosravirad, S. R., Poor, H. V., & Duong, T. Q. (2023). Joint communication and computation offloading for ultra-reliable and low-latency with multi-tier computing. IEEE Journal on Selected Areas in Communications, 41(2), 521–537. https://doi.org/10.1109/JSAC.2022.3227088

Van Huynh, D., Nguyen, V.-D., Dobre, O. A., Khosravirad, S. R., & Duong, T. Q. (2023). Adaptive service placement, task offloading and bandwidth allocation in task-oriented URLLC edge networks. ICC 2023 - IEEE International Conference on Communications, 5755–5760. https://doi.org/10.1109/ICC45041.2023.10279120

Van Huynh, D., Nguyen, V.-D., Khosravirad, S. R., & Duong, T. Q. (2022a). Fairness-aware latency minimisation in digital twin-aided edge computing with ultra-reliable and low-latency communications: A distributed optimisation approach (invited paper). 2022 56th Asilomar Conference on Signals, Systems, and Computers, 1045–1049. https://doi.org/10.1109/IEEECONF56349.2022.10051857

Van Huynh, D., Nguyen, V.-D., Khosravirad, S. R., & Duong, T. Q. (2022b). Minimising offloading latency for edge-cloud systems with ultra-reliable and low-latency communications. ICC 2022 - IEEE International Conference on Communications, 5122–5127. https://doi.org/10.1109/ICC45855.2022.9839148

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Van Huynh, D., Nguyen, V.-D., Khosravirad, S. R., Sharma, V., Dobre, O. A., Shin, H., & Duong, T. Q. (2022). URLLC edge networks with joint optimal user association, task offloading and resource allocation: A digital twin approach. IEEE Transactions on Communications, 70(11), 7669–7682. https://doi.org/10.1109/TCOMM.2022.3205692

Van Huynh, D., Nguyen, V.-D., Sharma, V., Dobre, O. A., & Duong, T. Q. (2022). Digital twin empowered ultra-reliable and low-latency communications-based edge networks in industrial IoT environment. ICC 2022 - IEEE International Conference on Communications, 5651–5656. https://doi.org/10.1109/ICC45855.2022.9838860

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