arXiv subjects

Trinh Van Chien

Publications and source records attributed to Trinh Van Chien.

At least 19 recordsLinked to original sources

arXiv2026

Joint Load Balancing and Transmit Power Control for Energy Efficiency Maximization in the Satellite-Cell-Free Massive MIMO Uplink

The seamless integration of non-terrestrial and terrestrial infrastructures is a key enabler for ubiquitous connectivity in next-generation (NG) wireless networks. We investigate a hybrid satellite-cell-free Massive MIMO system, where multiple low-Earth-orbit (LEO) satellites jointly serve users in unison with terrestrial access points (APs) under realistic imperfect channel state information and practical user association constraints. We first derive closed-form expressions of the uplink ergodic throughput by exploiting maximum ratio combining (MRC) for transmission over spatially correlated Rician fading channels. Our analysis reveals the characteristic impact of both user-satellite and user-AP association patterns on both the spectral efficiency and rate-fairness achieved. We then formulate an energy efficiency optimization problem under joint user association and power control. Since the problems are inherently NP-hard due to the binary nature of the user-association variables, we develop an improved Differential Evolution (IDE) framework that efficiently explores the feasible solutions in polynomial time. Numerical results validate our analysis and show that the proposed hybrid scheme substantially improves energy efficiency and network throughput. For large-scale scenarios, the DE framework provides practical user-satellite-AP association guidelines, enabling scalable performance gains.

cs.IT↗

arXiv2026

Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks

Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization. To address this challenge, we study a joint service placement, computational delegation, and power control (JSCP) problem to minimize the average end-to-end (e2e) latency. The resulting JSCP problem is a mixed-integer nonconvex and NP-hard optimization problem due to the strong coupling between discrete and continuous variables. To enable tractable optimization and stable system adaptation, we exploit the inherent difference in decision dynamics and decompose the problem into long-term system configuration and short-term resource allocation subproblems. Based on this formulation, we propose a two-timescale multi-layer deep reinforcement learning framework with a latent action space (2T-MDRL-LA) to jointly optimize service placement, user association, computational delegation, task offloading, and user transmit power. A latent action representation based on a variational autoencoder is introduced to efficiently compress the high-dimensional combinatorial action space. Simulation results demonstrate that the proposed framework effectively adapts to dynamic network conditions and achieves near-optimal performance compared to branch-and-bound solutions. It achieves up to a 20.8% reduction in average e2e latency and a 13% improvement in resource utilization over the scheme without the computational delegation, while converging approximately 50% faster than conventional proximal policy optimization.

cs.LG↗

arXiv2026

Deterministic versus Stochastic Optimization for Joint Path Planning and Dynamic Time Splitting in Multiple-UAV-Cached IoT Networks

This paper examines wireless-powered Internet of Things (IoT) networks involving multiple unmanned aerial vehicles (UAVs) equipped with backscatter and caching technologies to relay and transmit signals. For data communication and energy harvesting (EH), the source transmits information and power to UAVs using the dynamic time splitting (DTS) method. UAVs use harvested energy for passive communication (backscatter) and for active communication (transmitting information) to the destination. The primary objective is to maximize the total throughput by jointly optimizing the DTS ratio, trajectory, and transmission power, leveraging the UAVs' caching capability. This optimization problem is challenging due to its non-convexity. Therefore, an efficient alternating algorithm using the block coordinate descent (BCD) method is proposed to optimize each variable given the fixed values of the other parameters. By applying the Karush-Kuhn-Tucker (KKT) conditions, we derive a closed-form expression for the optimal DTS ratio, significantly reducing computation time. The optimal values for the other two parameters are determined using the BCD. In order to thoroughly assess the effectiveness of various solutions for the original problem, this paper introduces an approach leveraging a genetic algorithm (GA). The GA in this context employs a one-point crossover method, value mutation, and rank-based selection based on fitness values. Numerical results show that the BCD and GA achieve at least 31% throughput improvement over the benchmarks, with reduced computational time. These findings demonstrate the performance gain and practical feasibility of our solutions in caching-enabled UAV-aided IoT networks.

cs.IT↗

arXiv2026

Network-Assisted Full-Duplex Cell-Free Massive MIMO Systems Under Infeasible Circumstances

Cell-free massive multiple-input multiple-output is a potential candidate for future networks with pervasive connectivity by utilizing coherent joint transmission and distributed antenna arrays. This paper studies the exploitation of full-duplex communication for a distributed antenna array. Specifically, we derive a closed-form expression for the uplink and downlink ergodic spectral efficiency (SE) for a network where the APs can flexibly operate in either the full-duplex or half-duplex mode with linear processing and Rayleigh fading channels. A long-term total SE maximization problem is formulated subject to a network operation model and individual SE requirements with limited power budget. Due to the intrinsic nonconvexity and infeasible circumstances where some UEs might not be able to achieve the rate requirements, we adapt differential evolution to design a low computational complexity algorithm that can attain good power allocation and network operation mode in polynomial time. Numerical results demonstrate the effectiveness of our system design and proposed algorithm over state-of-the-art benchmarks with satisfactory service to the majority of UEs, although several ones may be unscheduled under harsh conditions.

eess.SP↗

arXiv2026

Advanced Quantum Annealing for the Bi-Objective Traveling Thief Problem: An $\varepsilon$-Constraint-based Approach

This paper addresses the Bi-Objective Traveling Thief Problem (BI-TTP), a challenging multi-objective optimization problem that requires the simultaneous optimization of travel cost and item profit. Conventional methods for the BI-TTP often face severe scalability issues due to the complex interdependence between routing and packing decisions, as well as the inherent complexity and large problem size. These difficulties render classical computing approaches increasingly inapplicable. To tackle this, we propose an advanced hybrid approach that combines quantum annealing (QA) with the $\varepsilon$-constraint method. Specifically, we reformulate the bi-objective problem into a single-objective formulation by restricting the second objective through adjustable $\varepsilon$-levels, determined within established upper and lower bounds. The resulting subproblem involves a sum of fractional terms, which is reformulated with auxiliary variables into an equivalent form. Subsequently, the equivalent formulation is transformed into a Quadratic Unconstrained Binary Optimization (QUBO) model, enabling direct solution via a quantum annealing (QA) solver. The solutions obtained from the quantum annealer are subsequently refined using a tailored heuristic procedure to further enhance overall performance. By leveraging the flexibility in selecting $\varepsilon$ parameters, our approach effectively captures a broad Pareto front, enhancing solution diversity. Experimental results on benchmark instances demonstrate that the proposed method effectively balances two objectives and outperforms baseline approaches in time efficiency.

quant-ph↗

arXiv2026

Energy Efficiency Maximization for Integrated Sensing and Communications in Satellite-UAV MIMO Systems

This paper investigates energy efficiency maximization in an integrated sensing and communication framework for satellite-UAV MIMO systems, where a LEO satellite and a UAV simultaneously serve ground users and perform target sensing. Both the satellite and UAV are equipped with uniform planar arrays of transmit antennas, enabling a distributed multi-user and multi-target architecture. We derive the achievable downlink throughput by considering that the high-altitude satellite maintains a line-of-sight (LoS) link with users, while adopting a probabilistic model for the UAV that accounts for the likelihood of both LoS and non-line-of-sight conditions. The energy efficiency maximization problem is formulated as a complex non-convex optimization problem, subject to power constraints, quality of service (QoS) requirements, and beampattern gain constraints for accurate sensing. To tackle this challenge, we propose an efficient alternating optimization algorithm capable of handling the complex search space and QoS guarantees. Numerical results across diverse scenarios with multiple users demonstrate that the proposed method achieves high energy efficiency while meeting both communication and sensing performance targets.

cs.IT↗

arXiv2026

Scalable Quantum Message Passing Graph Neural Networks for Next-Generation Wireless Communications: Architectures, Use Cases, and Future Directions

Graph Neural Networks (GNNs) are eminently suitable for wireless resource management, thanks to their scalability, but they still face computational challenges in large-scale, dense networks in classical computers. The integration of quantum computing with GNNs offers a promising pathway for enhancing computational efficiency because they reduce the model complexity. This is achieved by leveraging the quantum advantages of parameterized quantum circuits (PQCs), while retaining the expressive power of GNNs. However, existing pure quantum message passing models remain constrained by the limited number of qubits, hence limiting the scalability of their application to the wireless systems. As a remedy, we conceive a Scalable Quantum Message Passing Graph Neural Network (SQM-GNN) relying on a quantum message passing architecture. To address the aforementioned scalability issue, we decompose the graph into subgraphs and apply a shared PQC to each local subgraph. Importantly, the model incorporates both node and edge features, facilitating the full representation of the underlying wireless graph structure. We demonstrate the efficiency of SQM GNN on a device-to-device (D2D) power control task, where it outperforms both classical GNNs and heuristic baselines. These results highlight SQM-GNN as a promising direction for future wireless network optimization.

cs.IT↗

arXiv2026

Single- and Multi-Objective Stochastic Optimization for Next-Generation Networks in the Generative AI and Quantum Computing Era

Next Generation (NG) networks move beyond simply connecting devices to creating an ecosystem of connected intelligence, especially with the support of generative Artificial Intelligence (AI) and quantum computation. These systems are expected to handle large-scale deployments and high-density networks with diverse functionalities. As a result, there is an increasing demand for efficient and intelligent algorithms that can operate under uncertainty from both propagation environments and networking systems. Traditional optimization methods often depend on accurate theoretical models of data transmission, but in real-world NG scenarios, they suffer from high computational complexity in large-scale settings. Stochastic Optimization (SO) algorithms, designed to accommodate extremely high density and extensive network scalability, have emerged as a powerful solution for optimizing wireless networks. This includes various categories that range from model-based approaches to learning-based approaches. These techniques are capable of converging within a feasible time frame while addressing complex, large-scale optimization problems. However, there is currently limited research on SO applied for NG networks, especially the upcoming Sixth-Generation (6G). In this survey, we emphasize the relationship between NG systems and SO by eight open questions involving the background, key features, and lesson learned. Overall, our study starts by providing a detailed overview of both areas, covering fundamental and widely used SO techniques, spanning from single to multi-objective signal processing. Next, we explore how different algorithms can solve NG challenges, such as load balancing, optimizing energy efficiency, improving spectral efficiency, or handling multiple performance trade-offs. Lastly, we highlight the challenges in the current research and propose new directions for future studies.

cs.IT↗

arXiv2025

Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning

The vehicle routing problem (VRP) is a fundamental NP-hard task in intelligent transportation systems with broad applications in logistics and distribution. Deep reinforcement learning (DRL) with Graph Neural Networks (GNNs) has shown promise, yet classical models rely on large multi-layer perceptrons (MLPs) that are parameter-heavy and memory-bound. We propose a Quantum Graph Attention Network (Q-GAT) within a DRL framework, where parameterized quantum circuits (PQCs) replace conventional MLPs at critical readout stages. The hybrid model maintains the expressive capacity of graph attention encoders while reducing trainable parameters by more than 50%. Using proximal policy optimization (PPO) with greedy and stochastic decoding, experiments on VRP benchmarks show that Q-GAT achieves faster convergence and reduces routing cost by about 5% compared with classical GAT baselines. These results demonstrate the potential of PQC-enhanced GNNs as compact and effective solvers for large-scale routing and logistics optimization.

cs.LG↗

arXiv2025

Trustworthy GenAI over 6G: Integrated Applications and Security Frameworks

The integration of generative artificial intelligence (GenAI) into 6G networks promises substantial performance gains while simultaneously exposing novel security vulnerabilities rooted in multimodal data processing and autonomous reasoning. This article presents a unified perspective on cross-domain vulnerabilities that arise across integrated sensing and communication (ISAC), federated learning (FL), digital twins (DTs), diffusion models (DMs), and large telecommunication models (LTMs). We highlight emerging adversarial agents such as compromised DTs and LTMs that can manipulate both the physical and cognitive layers of 6G systems. To address these risks, we propose an adaptive evolutionary defense (AED) concept that continuously co-evolves with attacks through GenAI-driven simulation and feedback, combining physical-layer protection, secure learning pipelines, and cognitive-layer resilience. A case study using an LLM-based port prediction model for fluid-antenna systems demonstrates the susceptibility of GenAI modules to adversarial perturbations and the effectiveness of the proposed defense concept. Finally, we summarize open challenges and future research directions toward building trustworthy, quantum-resilient, and adaptive GenAI-enabled 6G networks.

cs.CR↗

arXiv2025

Fairness Designs for Load Balancing Optimization in Satellite-Cell-Free Massive MIMO Systems

Space-ground communication systems are important in providing ubiquitous services in a large area. This paper considers the fairness designs under a load-balancing framework with heterogeneous receivers comprising access points (APs) and a satellite. We derive an ergodic throughput of each user in the uplink data transmission for an arbitrary association pattern and imperfect channel state information, followed by a closed-form expression with the maximum-ratio combining and rich scattering environments. We further formulate a generic fairness optimization problem, subject to the optimal association patterns for all the users. Despite the combinatorial structure, the global optimal solution to the association patterns can be obtained by an exhaustive search for small-scale networks with several APs and users. We design a low computational complexity algorithm for large-scale networks based on evolutionary computation that obtains good patterns in polynomial time. Specifically, the genetic algorithm (GA) is adapted to the discrete feasible region and the concrete fairness metrics. We extensively observe the fairness design problem by incorporating transmit power control and propose a hybrid genetic algorithm to address the problem. Numerical results demonstrate that the association pattern to each user has a significant impact on the network throughput. Moreover, the proposed GA-based algorithm offers the same performance as an exhaustive search for small-scale networks, while it unveils interesting practical association patterns as the network dimensions go large. The load-balancing approach, combined with power control factors, significantly enhances system performance compared to conventional schemes and configurations with fixed factors.

cs.IT↗

arXiv2025

Secure Distributed RIS-MIMO over Double Scattering Channels: Adversarial Attack, Defense, and SER Improvement

There has been a growing trend toward leveraging machine learning (ML) and deep learning (DL) techniques to optimize and enhance the performance of wireless communication systems. However, limited attention has been given to the vulnerabilities of these techniques, particularly in the presence of adversarial attacks. This paper investigates the adversarial attack and defense in distributed multiple reconfigurable intelligent surfaces (RISs)-aided multiple-input multiple-output (MIMO) communication systems-based autoencoder in finite scattering environments. We present the channel propagation model for distributed multiple RIS, including statistical information driven in closed form for the aggregated channel. The symbol error rate (SER) is selected to evaluate the collaborative dynamics between the distributed RISs and MIMO communication in depth. The relationship between the number of RISs and the SER of the proposed system based on an autoencoder, as well as the impact of adversarial attacks on the system's SER, is analyzed in detail. We also propose a defense mechanism based on adversarial training against the considered attacks to enhance the model's robustness. Numerical results indicate that increasing the number of RISs effectively reduces the system's SER but leads to the adversarial attack-based algorithm becoming more destructive in the white-box attack scenario. The proposed defense method demonstrates strong effectiveness by significantly mitigating the attack's impact. It also substantially reduces the system's SER in the absence of an attack compared to the original model. Moreover, we extend the phenomenon to include decoder mobility, demonstrating that the proposed method maintains robustness under Doppler-induced channel variations.

cs.IT↗

arXiv2025

Graph Neural Networks for Next-Generation-IoT: Recent Advances and Open Challenges

Graph Neural Networks (GNNs) have emerged as a powerful framework for modeling complex interconnected systems, hence making them particularly well-suited to address the growing challenges of next-generation Internet of Things (NG-IoT) networks. Existing studies remain fragmented, and there is a lack of comprehensive guidance on how GNNs can be systematically applied to NG-IoT systems. As NG-IoT systems evolve toward 6G, they incorporate diverse technologies. These advances promise unprecedented connectivity, sensing, and automation but also introduce significant complexity, requiring new approaches for scalable learning, dynamic optimization, and secure, decentralized decision-making. This survey provides a comprehensive and forward-looking exploration of how GNNs can empower NG-IoT environments. We commence by exploring the fundamental paradigms of GNNs and articulating the motivation for their use in NG-IoT networks. Besides, we intrinsically connect GNNs with the family of low-density parity-check codes, modeling the NG-IoT as dynamic constrained graphs. We highlight the distinct roles of node-, edge-, and graph-level tasks in tackling key challenges and demonstrate the GNNs' ability to overcome the limitations of traditional optimization. We examine the application of GNNs across core NG-enabling technologies and their integration with distributed frameworks to support privacy-preservation and distributed intelligence. We then delve into the challenges posed by adversarial attacks, offering insights into defense mechanisms. Lastly, we examine how GNNs can be integrated with emerging technologies. Our findings highlight the transformative potential of GNNs in improving efficiency, scalability, and security. Finally, we summarize the key lessons learned and outline promising future research directions, along with a set of design guidelines tailored for NG-IoT applications.

cs.IT↗

arXiv2025

Clustering Strategies in Satellite-Aided Communications

With the rapid advancement of next-generation satellite networks, addressing clustering tasks, user grouping, and efficient link management has become increasingly critical to optimize network performance and reduce interference. In this paper, we provide a comprehensive overview of modern clustering approaches based on machine learning and heuristic algorithms. The experimental results indicate that improved machine learning techniques and graph theory-based methods deliver significantly better performance and scalability than conventional clustering methods, such as the pure clustering algorithm examined in previous research. These advantages are especially evident in large-scale satellite network scenarios. Furthermore, the paper outlines potential research directions and discusses integrated, multi-dimensional solutions to enhance adaptability and efficiency in future satellite communication.

cs.IT↗

arXiv2025

Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO

Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.

cs.IT↗

arXiv2025

Joint Access Point Activation and Power Allocation for Cell-Free Massive MIMO Aided ISAC Systems

Cell-free massive multiple-input multiple-output (MIMO)-aided integrated sensing and communication (ISAC) systems are investigated where distributed access points jointly serve users and sensing targets. We demonstrate that only a subset of access points (APs) has to be activated for both tasks, while deactivating redundant APs is essential for power savings. This motivates joint active AP selection and power control for optimizing energy efficiency. The resultant problem is a mixed-integer nonlinear program (MINLP). To address this, we propose a model-based Branch-and-Bound approach as a strong baseline to guide a semi-supervised heterogeneous graph neural network (HetGNN) for selecting the best active APs and the power allocation. Comprehensive numerical results demonstrate that the proposed HetGNN reduces power consumption by 20-25\% and runs nearly 10,000 times faster than model-based benchmarks.

cs.IT↗

arXiv2025

Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems

A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.

cs.IT↗

arXiv2025

A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization

Mobile edge computing (MEC) allows appliances to offload workloads to neighboring MEC servers that have the potential for computation-intensive tasks with limited computational capabilities. This paper studied how deep reinforcement learning (DRL) algorithms are used in an MEC system to find feasible decentralized dynamic computation offloading strategies, which leads to the construction of an extensible MEC system that operates effectively with finite feedback. Even though the Deep Deterministic Policy Gradient (DDPG) algorithm, subject to their knowledge of the MEC system, can be used to allocate powers of both computation offloading and local execution, to learn a computation offloading policy for each user independently, we realized that this solution still has some inherent weaknesses. Hence, we introduced a new approach for this problem based on the Twin Delayed DDPG algorithm, which enables us to overcome this proneness and investigate cases where mobile users are portable. Numerical results showed that individual users can autonomously learn adequate policies through the proposed approach. Besides, the performance of the suggested solution exceeded the conventional DDPG-based power control strategy.

cs.IT↗