arXiv subjects

Ngoc-Tan Nguyen

Publications and source records attributed to Ngoc-Tan Nguyen.

6 recordsLinked to original sources

arXiv2025

Collaborative Intelligence for UAV-Satellite Network Slicing: Towards a Joint QoS-Energy-Fairness MADRL Optimization

Non terrestrial networks are critical for achieving global 6G coverage, yet efficient resource management in aerial and space environments remains challenging due to limited onboard power and dynamic operational conditions. Network slicing offers a promising solution for spectrum optimization in UAV based systems serving heterogeneous service demands. For that, this paper proposes a hierarchical network slicing framework for UAV satellite integrated networks supporting eMBB, URLLC, and mMTC services. Specifically, we formulate a joint optimization of UAV trajectory, transmission power, and spectrum allocation as a decentralized partially observable Markov decision process that ensures quality of service while minimizing energy consumption and maximizing resource fairness. To address the computational intractability and partial observability, we develop a multi agent deep reinforcement learning solution under the centralized training and decentralized execution paradigm. In the proposed system, UAV agents act as distributed actors coordinated by a shared critic operating with multi head attention mechanism at a low Earth orbit satellite. Experimental results then demonstrate that our approach outperforms existing methods by up to 33% in cumulative reward while achieving superior energy efficiency and fairness.

cs.NI↗

arXiv2025

Multi-Agent Deep Reinforcement Learning for Collaborative UAV Relay Networks under Jamming Atatcks

The deployment of Unmanned Aerial Vehicle (UAV) swarms as dynamic communication relays is critical for next-generation tactical networks. However, operating in contested environments requires solving a complex trade-off, including maximizing system throughput while ensuring collision avoidance and resilience against adversarial jamming. Existing heuristic-based approaches often struggle to find effective solutions due to the dynamic and multi-objective nature of this problem. This paper formulates this challenge as a cooperative Multi-Agent Reinforcement Learning (MARL) problem, solved using the Centralized Training with Decentralized Execution (CTDE) framework. Our approach employs a centralized critic that uses global state information to guide decentralized actors which operate using only local observations. Simulation results show that our proposed framework significantly outperforms heuristic baselines, increasing the total system throughput by approximately 50% while simultaneously achieving a near-zero collision rate. A key finding is that the agents develop an emergent anti-jamming strategy without explicit programming. They learn to intelligently position themselves to balance the trade-off between mitigating interference from jammers and maintaining effective communication links with ground users.

cs.NI↗

arXiv2025

Turning Threat into Opportunity: DRL-Powered Anti-Jamming via Energy Harvesting in UAV-Disrupted Channels

The open and broadcast nature of wireless communication systems, while enabling ubiquitous connectivity, also exposes them to jamming attacks that may critically compromise network performance or disrupt service availability. The proliferation of Unmanned Aerial Vehicles (UAVs) introduces a new dimension to this threat, as UAVs can act as mobile, intelligent jammers capable of launching sophisticated attacks by leveraging Line-of-Sight (LoS) channels and adaptive strategies. This paper addresses a critical challenge of countering intelligent UAV jamming in the context of energy-constrained ambient backscatter communication systems. Traditional anti-jamming techniques often fall short against such dynamic threats or are unsuitable for low-power backscatter devices. Hence, we propose a novel anti-jamming framework based on Deep Reinforcement Learning (DRL) that empowers the transmitter to not only defend against but also strategically exploit the UAV's jamming signals. In particular, our approach allows the transmitter to learn an optimal policy for switching between active transmission, energy harvesting from the jamming signal, and backscattering information using the jammer's own emissions. We then formulate the problem as a Markov Decision Process (MDP) and employ a Deep Q-Network (DQN) to derive the optimal operational strategy. Simulation results demonstrate that our DQN-based method significantly outperforms conventional Q-learning in convergence speed and surpasses a greedy anti-jamming strategy in terms of average throughput, packet loss rate, and packet delivery ratio.

cs.NI↗

arXiv2025

Quantum Reinforcement Learning for 6G and Beyond Wireless Networks

While 5G is being deployed worldwide, 6G is receiving increasing attention from researchers to meet the growing demand for higher data rates, lower latency, higher density, and seamless communications worldwide. To meet the stringent requirements of 6G wireless communications networks, AI-integrated communications have become an indispensable part of supporting 6G systems with intelligence, automation, and big data training capabilities. However, traditional artificial intelligence (AI) systems are difficult to meet the stringent latency and high throughput requirements of 6G with limited resources. In this article, we summarize, analyze, discuss the potential, and benefits of Quantum Reinforcement Learning (QRL) in 6G. As an example, we show the superiority of QRL in dynamic spectrum access compared to the conventional Deep Reinforcement Learning (DRL) approach. In addition, we provide an overview of what DRL has accomplished in 6G and its challenges and limitations. From there, we introduce QRL and potential research directions that should continue to be of interest in 6G. To the best of our knowledge, this is the first review and vision article on QRL for 6G wireless communication networks.

cs.NI↗

arXiv2020

Enabling and Emerging Technologies for Social Distancing: A Comprehensive Survey and Open Problems

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. To that end, we first provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effective and can be widely adopted in practice to keep distance, encourage, and enforce social distancing in general. After that, other emerging and related technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we provide important open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in its standard architecture/design.

physics.soc-ph↗

arXiv2020

Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-based IoT Networks

Future IoT networks consist of heterogeneous types of IoT devices (with various communication types and energy constraints) which are assumed to belong to an IoT service provider (ISP). To power backscattering-based and wireless-powered devices, the ISP has to contract with an energy service provider (ESP). This article studies the strategic interactions between the ISP and its ESP and their implications on the joint optimal time scheduling and energy trading for heterogeneous devices. To that end, we propose an economic framework using the Stackelberg game to maximize the network throughput and energy efficiency of both the ISP and ESP. Specifically, the ISP leads the game by sending its optimal service time and energy price request (that maximizes its profit) to the ESP. The ESP then optimizes and supplies the transmission power which satisfies the ISP's request (while maximizing ESP's utility). To obtain the Stackelberg equilibrium (SE), we apply a backward induction technique which first derives a closed-form solution for the ESP. Then, to tackle the non-convex optimization problem for the ISP, we leverage the block coordinate descent and convex-concave procedure techniques to design two partitioning schemes (i.e., partial adjustment (PA) and joint adjustment (JA)) to find the optimal energy price and service time that constitute local SEs. Numerical results reveal that by jointly optimizing the energy trading and the time allocation for heterogeneous IoT devices, one can achieve significant improvements in terms of the ISP's profit compared with those of conventional transmission methods. Different tradeoffs between the ESP's and ISP's profits and complexities of the PA/JA schemes can also be numerically tuned. Simulations also show that the obtained local SEs approach the socially optimal welfare when the ISP's benefit per transmitted bit is higher than a given threshold.

cs.NI↗