arXiv subjects

Dinh-Hieu Tran

Publications and source records attributed to Dinh-Hieu Tran.

17 recordsLinked to original sources

arXiv2026

UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach

This paper investigates the unmanned aerial vehicle (UAV)-assisted resilience perspective in the 6G network energy saving (NES) scenario. More specifically, we consider multiple ground base stations (GBSs) and each GBS has three different sectors/cells in the terrestrial networks, and multiple cells may become inactive due to unexpected events such as power outages, disasters, hardware failures, or erroneous energy-saving decisions made by external network management systems. During the time required to reactivate these cells, UAVs are deployed to temporarily restore user service. To address this, we propose a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to enable UAV-assisted communication by jointly optimizing UAV trajectories, transmission power, and user-UAV association under a sleeping ground base station (GBS) strategy. This framework aims to ensure the resilience of active users in the network and the long-term operability of UAVs. Specifically, it maximizes service coverage for users during power outages or NES zones, while minimizing the energy consumption of UAVs. Simulation results demonstrate that the proposed MADDPG policy consistently achieves high coverage ratio across different testing episodes, outperforming other baselines. Moreover, the MADDPG framework attains the lowest total energy consumption, while maintaining a comparable user service rate. These results confirm the effectiveness of the proposed approach in achieving a superior trade-off between energy efficiency and service performance, supporting the development of sustainable and resilient UAV-assisted cellular networks.

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arXiv2026

Resilience Optimization in 6G and Beyond Integrated Satellite-Terrestrial Networks: A Deep Reinforcement Learning Approach

Ensuring network resilience in 6G and beyond is essential to maintain service continuity during base station (BS) outages due to failures, disasters, attacks, or energy-saving operations. This paper proposes a novel resilience optimization framework for integrated satellite-terrestrial networks (ISTNs), leveraging low Earth orbit (LEO) satellites to assist users when terrestrial BSs are unavailable. Specifically, we develop a realistic multi-cell model incorporating user association, antenna downtilt adaptation, power control, heterogeneous traffic demands, and dynamic user distribution. The objective is to maximize of the total user rate in the considered area by optimizing the BS's antenna tilt, transmission power, user association to neighboring BS or to a LEO satellite with a minimum number of successfully served user satisfaction constraint, defined by rate and Reference Signal Received Power (RSRP) requirements. To solve the non-convex, NP-hard problem, we design a deep Q-network (DQN)-based algorithm to learn network dynamics to maximize throughput while minimizing LEO satellite usage, thereby limiting reliance on links with longer propagation delays and prolonging satellite operational lifetime. Simulation results confirm that our approach significantly outperforms the benchmark one.

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arXiv2026

Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery

Modern Earth Observation (EO) systems increasingly rely on high-resolution imagery to support critical applications such as environmental monitoring, disaster response, and land-use analysis. Although these applications benefit from detailed visual data, the resulting data volumes impose significant challenges on satellite communication systems constrained by limited bandwidth, power, and dynamic link conditions. To address these limitations, this paper investigates Deep Joint Source-Channel Coding (DJSCC) as an effective source-channel paradigm for the transmission of EO imagery. We focus on two complementary aspects of semantic loss in DJSCC-based systems. First, a reconstruction-centric framework is evaluated by analyzing the semantic degradation of reconstructed images under varying compression ratios and channel signal-to-noise ratios (SNR). Second, a task-oriented framework is developed by integrating DJSCC with lightweight, application-specific models (e.g., EfficientViT), with performance measured using downstream task accuracy rather than pixel-level fidelity. Based on extensive empirical analysis, we propose a unified semantic loss framework that captures both reconstruction-centric and task-oriented performance within a single model. This framework characterizes the implicit relationship between JSCC compression, channel SNR, and semantic quality, offering actionable insights for the design of robust and efficient EO imagery transmission under resource-constrained satellite links.

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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.

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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.

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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.

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arXiv2025

Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning

Spectrum access is an essential problem in device-to-device (D2D) communications. However, with the recent growth in the number of mobile devices, the wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. To address this problem, this paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals to transmit their data when the shared spectrum is occupied by mobile users. To obtain the optimal spectrum access policy, i.e., stay idle or access the shared spectrum and perform active transmissions or backscattering ambient RF signals for transmissions, to maximize the average throughput for D2D users, deep reinforcement learning (DRL) can be adopted. However, DRL-based solutions may require long training time due to the curse of dimensionality issue as well as complex deep neural network architectures. For that, we develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters compared to DRL thanks to the quantum superposition and quantum entanglement principles. Specifically, instead of using conventional deep neural networks, the proposed quantum RL algorithm uses a parametrized quantum circuit to approximate an optimal policy. Extensive simulations then demonstrate that the proposed solution not only can significantly improve the average throughput of D2D devices when the shared spectrum is busy but also can achieve much better performance in terms of convergence rate and learning complexity compared to existing DRL-based methods.

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arXiv2025

Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems

With the rapid development of technology, the number of smart mobile users is increasing, accompanied by growing demands from applications such as virtual/augmented reality (VR/XR), remote surgery, autonomous vehicles, and real-time holographic communications, all of which require high transmission rates and ultra-low latency in 6G and beyond networks (6G+). This poses enormous challenges in efficiently deploying large-scale networks, including network design, planning, troubleshooting, optimization, and maintenance, without affecting the user experience. Network Digital Twin (NDT) has emerged as a potential solution, enabling the creation of a virtual model that reflects the actual network, supporting the simulation of various network designs, applying diverse operating policies, and reproducing complex fault scenarios under real-world conditions. This motivate us for this study, where we provide a comprehensive survey of NDT in the context of 6G+, covering areas such as radio access networks (RAN), transport networks, 5G core networks and beyond (5GCORE+), cloud/edge computing, applications (blockchain, health system, manufacturing, security, and vehicular networks), non-terrestrial networks (NTNs), and quantum networks, from both academic and industrial perspectives. In particular, we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT. Then, we provide an extensive review of foundation technologies such as transport networks, cloud/edge computing, applications, NTNs, and quantum networks in NDT. Finally, we discuss the key challenges, open issues, and future research directions for NDT in the context of 6G+.

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arXiv2024

Deep Reinforcement Learning for Network Energy Saving in 6G and Beyond Networks

Network energy saving has received great attention from operators and vendors to reduce energy consumption and CO2 emissions to the environment as well as significantly reduce costs for mobile network operators. However, the design of energy-saving networks also needs to ensure the mobile users' (MUs) QoS requirements such as throughput requirements (TR). This work considers a mobile cellular network including many ground base stations (GBSs), and some GBSs are intentionally turned off due to network energy saving (NES) or crash, so the MUs located in these outage GBSs are not served in time. Based on this observation, we propose the problem of maximizing the total achievable throughput in the network by optimizing the GBSs' antenna tilt and adaptive transmission power with a given number of served MUs satisfied. Notice that, the MU is considered successfully served if its Reference Signal Received Power (RSRP) and throughput requirement are satisfied. The formulated optimization problem becomes difficult to solve with multiple binary variables and non-convex constraints along with random throughput requirements and random placement of MUs. We propose a Deep Q-learning-based algorithm to help the network learn the uncertainty and dynamics of the transmission environment. Extensive simulation results show that our proposed algorithm achieves much better performance than the benchmark schemes.

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arXiv2024

Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN

Resilience is defined as the ability of a network to resist, adapt, and quickly recover from disruptions, and to continue to maintain an acceptable level of services from users' perspective. With the advent of future radio networks, including advanced 5G and upcoming 6G, critical services become integral to future networks, requiring uninterrupted service delivery for end users. Unfortunately, with the growing network complexity, user mobility and diversity, it becomes challenging to scale current resilience management techniques that rely on local optimizations to large dense network deployments. This paper aims to address this problem by globally optimizing the resilience of a dense multi-cell network based on multi-agent deep reinforcement learning. Specifically, our proposed solution can dynamically tilt cell antennas and reconfigure transmit power to mitigate outages and increase both coverage and service availability. A multi-objective optimization problem is formulated to simultaneously satisfy resiliency constraints while maximizing the service quality in the network area in order to minimize the impact of outages on neighbouring cells. Extensive simulations then demonstrate that with our proposed solution, the average service availability in terms of user throughput can be increased by up to 50-60% on average, while reaching a coverage availability of 99% in best cases.

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arXiv2024

Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications

Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era.

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arXiv2022

Security and Reliability Analysis of Satellite-Terrestrial Multi-Relay Networks with Imperfect CSI

This work investigates the security and reliability analysis for a novel satellite-terrestrial (SatTer) network. Specifically, a satellite attempts to transmit confidential information to a ground user (GU) via the support of multiple relay nodes in the presence of an eavesdropper that tries to overhear the information. A friendly jammer is deployed to improve the secure transmission between the satellite and the relays. Furthermore, satellite-to-relay generalized Rician fading channels and imperfect channel state information (CSI) are deployed to examine a general system model. In this context, the closed-formed expressions for the outage probability (OP) and intercept probability (IP) are derived corresponding to an amplify-and-forward (AF)-based relaying scheme, which is challenging and has not been studied before. Finally, the exactness of the mathematical analyses is validated through Monte Carlo simulations. Furthermore, the effects of various key parameters (e.g., channel estimation errors, satellite's transmit power, relay's transmit power, number of relays, and fading severity parameter) are examined.

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arXiv2022

Security-Reliability Trade-Off Analysis for SWIPT- and AF-Based IoT Networks with Friendly Jammers

Radio-frequency (RF) energy harvesting (EH) in wireless relaying networks has attracted considerable recent interest, especially for supplying energy to relay nodes in Internet-of-Things (IoT) systems to assist the information exchange between a source and a destination. Moreover, limited hardware, computational resources, and energy availability of IoT devices have raised various security challenges. To this end, physical layer security (PLS) has been proposed as an effective alternative to cryptographic methods for providing information security. In this study, we propose a PLS approach for simultaneous wireless information and power transfer (SWIPT)-based half-duplex (HD) amplify-and-forward (AF) relaying systems in the presence of an eavesdropper. Furthermore, we take into account both static power splitting relaying (SPSR) and dynamic power splitting relaying (DPSR) to thoroughly investigate the benefits of each one. To further enhance secure communication, we consider multiple friendly jammers to help prevent wiretapping attacks from the eavesdropper. More specifically, we provide a reliability and security analysis by deriving closed-form expressions of outage probability (OP) and intercept probability (IP), respectively, for both the SPSR and DPSR schemes. Then, simulations are also performed to validate our analysis and the effectiveness of the proposed schemes. Specifically, numerical results illustrate the non-trivial trade-off between reliability and security of the proposed system. In addition, we conclude from the simulation results that the proposed DPSR scheme outperforms the SPSR-based scheme in terms of OP and IP under the influences of different parameters on system performance.

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arXiv2021

Controlling Smart Propagation Environments: Long-Term versus Short-Term Phase Shift Optimization

Reconfigurable intelligent surfaces (RISs) have recently gained significant interest as an emerging technology for future wireless networks. This paper studies an RIS-assisted propagation environment, where a single-antenna source transmits data to a single-antenna destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, closed-form expressions for the coverage probability and ergodic rate are derived. We use numerical simulations to analyze and compare against analytic results in finite samples. Also, we show that the considered optimal phase shift designs outperform several heuristic benchmarks.

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arXiv2021

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

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 iff 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.

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arXiv2021

Satellite- and Cache-assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks

This paper considers LEO satellite- and cache-assisted UAV communications for content delivery in terrestrial networks, which shows great potential for next-generation systems to provide ubiquitous connectivity and high capacity. Specifically, caching is provided by the UAV to reduce backhaul congestion, and the LEO satellite supports the UAV's backhaul link. In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV's resource allocation, and trajectory while cache capacity and flight time are limited. The formulated problem is challenging to solve directly due to its non-convexity and combinatorial nature. To find a solution, the problem is decomposed into three sub-problems: (1) cache placement optimization with fixed UAV resources and trajectory, followed by (2) the UAV resources optimization with fixed cache placement vector and trajectory, and finally, (3) we optimize the UAV trajectory with fixed cache placement and UAV resources. Based on the solutions of sub-problems, an efficient alternating algorithm is proposed utilizing the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Simulation results show that the max-min throughput and total achievable throughput enhancement can be achieved by applying our proposed algorithm instead of other benchmark schemes.

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arXiv2020

Trajectory Design for Energy Minimization in UAV-enabled Wireless Communications with Latency Constraints

This paper studies energy-efficient unmanned aerial vehicle (UAV)-enabled wireless communications, where the UAV acts as a flying base station (BS) to serve the ground users (GUs) within some predetermined latency constraints, e.g., requested timeout (RT). Our goal is to design the UAV trajectory to minimize the total energy consumption while satisfying the RT requirement and energy budget, which is accomplished via jointly optimizing the trajectory and UAV's velocities along subsequent hops. The corresponding optimization problem is difficult to solve due to its non-convexity and combinatorial nature. To overcome this difficulty, we solve the original problem via two consecutive steps. Firstly, we propose two algorithms, namely heuristic search, and dynamic programming (DP) to obtain a feasible set of trajectories without violating the GU's RT requirements based on the traveling salesman problem with time window (TSPTW). Then, they are compared with exhaustive search and traveling salesman problem (TSP) used as reference methods. While the exhaustive algorithm achieves the best performance at a high computation cost, the heuristic algorithm exhibits poorer performance with low complexity. As a result, the DP is proposed as a practical trade-off between the exhaustive and heuristic algorithms. Specifically, the DP algorithm results in near-optimal performance at a much lower complexity. Secondly, for given feasible trajectories, we propose an energy minimization problem via a joint optimization of the UAV's velocities along subsequent hops. Finally, numerical results are presented to demonstrate the effectiveness of our proposed algorithms. ...

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