arXiv subjects

Hien Quoc Ngo

Publications and source records attributed to Hien Quoc Ngo.

At least 19 recordsLinked to original sources

arXiv2026

Massive MIMO ISAC Under Target-Angle Uncertainty: CRLB Outage Analysis and Robust Resource Allocation

In integrated sensing and communications (ISAC), the same spectral and hardware resources are shared for two functionalities. Most ISAC designs assume perfect target-angle information neglecting angle estimation errors, which introduce steering-vector mismatches, degrade sensing accuracy, and may invalidate deterministic sensing guarantees. This paper investigates monostatic massive multiple-input-multiple-output (MIMO) ISAC systems under imperfect target-angle estimates. We derive closed-form expressions for the Cramér-Rao lower bounds (CRLBs) of target azimuth and elevation estimates in the presence of angle uncertainty. We characterize the cumulative distribution functions and outage probabilities of the CRLBs under Gaussian, generalized uniform, and von Mises angle-error models. Our analysis reveals that, in the small-error regime, the CRLBs increase quadratically with the angle errors due to transmit steering-vector mismatch. To ensure reliable sensing, we propose a robust power allocation framework that jointly optimizes pilot training and communications/sensing transmission powers to maximize the communications sum rate while satisfying CRLB outage constraints. The resulting nonconvex problem is solved using an alternating-optimization algorithm based on successive convex approximation. Numerical results validate the developed analysis and show that the proposed robust design reduces azimuth and elevation CRLB outage probabilities by up to $60\%$ compared with conventional non-robust schemes. It attains up to $45\%$ higher sum rates than the non-robust design under strict CRLB thresholds.

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arXiv2026

Tri-Hybrid Beamforming Design for DMA-Aided Secure ISAC Systems

This paper proposes a tri-hybrid beamforming scheme for secure integrated sensing and communication (ISAC) with a dynamic metasurface antenna (DMA) architecture, where the base station (BS) is capable of communicating with legitimate users and sensing the target. There is also an eavesdropper in the system intending to eavesdrop on the confidential information. The tri-hybrid beamforming design problem is formulated with the objective of maximizing the sensing signal-to-noise ratio (SNR) under the constraints of secrecy spectral efficiency (SSE), transmit power, and physical structure limitations. We first solve the problem and obtain the optimized fully-digital beamforming solution through successive convex approximation (SCA) and semidefinite relaxation (SDR) approaches. A triple alternating optimization scheme is then developed to iteratively optimize the digital, analog, and DMA beamformers, progressively approximating the fully-digital solution. Numerical results demonstrate that the proposed secure tri-hybrid beamforming design for DMA-aided ISAC improves the sensing SNR by approximately 3 dB compared to a tri-hybrid beamforming scheme with a fixed DMA electromagnetic design.

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arXiv2026

Network Availability Enhancement in Low-Altitude HetNets: A Cross-Layer Design Perspective

This paper proposes a computing-communication resource interchange method to enhance network availability (NA) in low-altitude heterogeneous networks (LA-HetNets). In these networks, communication resource conflicts and imbalances, caused by extreme heterogeneity (diverse mobility, mixed delays, and hybrid transmission), and cross-regional traffic, reduce reliability and lead to unavailability. Restoring NA requires additional communication resources, yet dynamic cross-regional scheduling is limited, making locally redundant computing resources an alternative to reduce communication resource overhead. While computing resources address medium access control (MAC)-layer unreliability, physical (PHY)-layer functionalities still rely on communication resources. Thus, it remains unclear whether increasing computing resources alone can achieve target NA, especially under greater heterogeneity. We elaborate on the impact of heterogeneity on NA and show that expanding computing resources alone cannot meet target NA under high heterogeneity, as NA degrades sharply due to increased communication capability demands. To overcome this, we propose a cross-layer optimization method enabling computing-communication resource interchange to address both MAC- and PHY-layer unreliability. By reducing processing delays with computing resources while ensuring MAC-layer reliability, our method extends PHY-layer transmission delay and expands communication resources. Simulations demonstrate our approach's superiority in achieving target NA under greater heterogeneity, revealing that computing-communication resource interchange fulfills expanding communication capability demands more effectively than conventional resource overhead reduction.

cs.NI↗

arXiv2026

Positioning with Flexible Reflectors: Solution and Performance Analysis

Flexible reflectors (FRs) have emerged as a low-cost and energy-efficient solution for reshaping electromagnetic propagation environments across a wide range of applications. This paper investigates FR-swarm-assisted target localization in scenarios where line-of-sight (LoS) paths are unavailable. By leveraging the virtual LoS paths created by the FRs, a simple yet accurate estimator is proposed for localization under severe blockage conditions. To characterize the performance limits of the proposed scheme, we derive the Cramer-Rao lower bound (CRLB) and use it to optimize the positions and orientations of the FRs. Furthermore, by accounting for random FR deployment, we characterize the CRLB distribution and reveal how different network configurations affect localization accuracy. Simulation results demonstrate that the developed scheme closely approaches the CRLB performance, while the derived analytical results provide useful guidelines for FR deployment and network design.

cs.IT↗

arXiv2026

Hybrid STAR-RIS Architecture for Joint Localization, Communication, and Power Transfer

We propose a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) architecture with dynamically switched active and passive elements to support joint localization, communication, and wireless power transfer (WPT). We first pursue a parallel factor analysis with the alternating least squares (PARAFAC-ALS)-based tensor decomposition approach that decouples the base station (BS)-reconfigurable intelligent surface (RIS) and RIS-user channels, thereby enabling low-overhead channel acquisition. Based on this, we formulate a system energy efficiency (EE) maximization problem, subject to the spectral efficiency (SE) requirements of communication users, sensing signal-to-interference-plus-noise ratio constraints, and the nonlinear energy harvesting requirements of energy-harvesting users. The optimization problem is nonconvex since the transmit power allocation, STAR-RIS coefficients, and active/passive mode assignments are tightly coupled in both the objective and constraints. We address this issue by alternating between two subproblems, and solving them via fractional programming, successive convex approximation and a multi-seed greedy strategy employed as an initialization step. Numerical results demonstrate that selectively activating a small, well-chosen subset of STAR-RIS elements achieves 1.5 to 3 times EE improvements compared with fully passive/active architectures, while satisfying communication, sensing, and power-transfer requirements.

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arXiv2026

Deep-Unfolded Accelerated Projected Gradient for Energy-Efficient Cell-Free Massive MIMO

This paper investigates energy efficiency (EE) maximization for the downlink of cell-free massive multiple-input multiple-output systems under quality-of-service and per-access point power constraints. We first derive closed-form gradient expressions of the objective function with respect to the power allocation coefficients, and then propose an accelerated projected gradient (APG) approach to solve this problem. To reduce the computational complexity and runtime of APG, we propose a deep-unfolded APG framework that maps iterative APG updates onto a finite number of neural network layers, where parameters such as step sizes and penalty coefficients are learned from data. The proposed approach produces power allocation solutions through a fixed number of gradient-based updates without the need for line search or manual parameter tuning. Numerical results show that the method achieves EE performance comparable to the iterative APG approach while requiring significantly lower computational cost, with up to a 30-fold reduction in floating-point operations under the considered system settings.

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arXiv2026

Power-Efficient XL-MIMO Design for Mixed Near- and Far-Field SWIPT Systems

This paper examines the power consumption (PC) efficiency of a mixed near- and far-field (MF) simultaneous wireless information and power transfer (SWIPT) system underpinned by a hybrid beamforming (HB)-based modular extra-large multiple-input-multiple output (XL-MIMO) array. Multiple information decoding (ID) and energy harvesting (EH) users are served by multiple constituent subarrays in both the near-field (NF) and far-field (FF) region of the transmit array. A novel decision method is proposed for accurate classification of different field users using Frobenius norm-based frequency correlation of the least square (LS) channel estimates. The NF spatial non-stationarities (SnS) effects entail distinct electromagnetic (EM) visibility regions (VRs), which can be customized to employ strategic activation of the constituent XL-MIMO subarrays. We formulate a two-tier joint optimization problem to minimize the overall PC, considering the power allocation (PA) for both ID and EH users in addition to the subarray activation (SA). This challenging mixed-integer problem is transformed into computationally tractable formulations, accompanied by the development of well-optimized algorithms. Our simulation results demonstrate an overall PC reduction for our proposed PA-SA-HB scheme by up to 93% against the equal PA with full array (FA) and up to 18% with respect to the PA-FA-HB case.

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arXiv2026

DRL-Based Spectrum Sharing for RIS-Aided Local High-Quality Wireless Networks

This paper investigates a smart spectrum-sharing framework for reconfigurable intelligent surface (RIS)-aided local high-quality wireless networks (LHQWNs) within a mobile network operator (MNO) ecosystem. Although RISs are often considered harmful due to interference, this work shows that properly controlled RISs can enhance quality of service (QoS). The proposed system enables temporary spectrum access for multiple vertical service providers (VSPs) by dynamically allocating radio resources. The spectrum is divided into dedicated subchannels assigned to individual VSPs and reusable subchannels shared among multiple VSPs, while RIS improves propagation conditions and zero-forcing (ZF) precoding is adopted at the multiantenna base station (BS) to cancel inter-user interference. We formulate a multi-VSP utility maximization problem that jointly optimizes subchannel assignment, transmit power, and RIS phase configuration while accounting for spectrum access costs, RIS leasing costs, and QoS constraints. The resulting mixed-integer non-linear program (MINLP) is modeled as a Markov decision process (MDP) and solved using deep reinforcement learning (DRL). Deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) algorithms are developed and compared. Numerical results show that SAC generally outperforms DDPG in convergence, stability, and utility, particularly in larger-scale scenarios. In the reduced-scale ablation study, both joint DRL methods outperform the heuristic benchmark by at least 6.6% in final moving-average reward.

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arXiv2026

Massive MIMO-OFDM ISAC for Sparse ISAR Imaging: Joint Power and Subcarrier Allocation

This paper investigates a massive multiple-input multiple-output (mMIMO) orthogonal frequency-division multiplexing (OFDM) framework for integrated sensing and communication (ISAC) with inverse synthetic aperture radar (ISAR) imaging, supporting applications such as the Internet of Things (IoT). A dual-function architecture combines communication precoding and dedicated sensing beamforming to enable simultaneous downlink communication and ISAR imaging. Due to intermittent pilot transmission and sparse sensing-subcarrier activation, the received echoes provide incomplete measurements, resulting in a sparse-aperture ISAR reconstruction problem. To address this issue, an adaptive reweighted two-dimensional alternating direction method of multipliers (ADMM) algorithm is developed for high-resolution image recovery from sparse observations. A joint resource-allocation framework is also proposed to optimize communication-subcarrier assignment, sensing-subcarrier selection, and transmit power allocation subject to communication quality-of-service and sensing constraints. Exploiting channel hardening, analytical full-band sensing benchmarks based solely on statistical channel state information (CSI) are derived for maximum-ratio (MR) and zero-forcing (ZF) precoding, while a soft actor-critic (SAC)-based method is developed for sparse-sensing resource allocation. Numerical results show that the proposed adaptive ADMM algorithm improves sparse ISAR reconstruction over conventional methods. The SAC-based design also achieves substantial gains in sum spectral efficiency over the full-band benchmarks while satisfying communication and sensing constraints, thereby revealing the tradeoff between ISAR reconstruction accuracy and communication spectral efficiency.

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arXiv2026

ML-Assisted Bulk Resource Allocation: Custom Outage-Based Loss Function and Reliability Analysis

Machine learning (ML)-assisted outage-based resource allocation has recently emerged as an effective alternative to conventional scheduling methods in reliability-critical wireless systems. However, existing approaches are fundamentally limited to single-resource allocation, whereas modern and emerging systems increasingly require the simultaneous allocation of multiple resources to meet aggregate rate and reliability constraints. In this paper, we extend outage-based learning to the bulk resource allocation regime, where a user requires at least $D$ reliable resources from a pool of $R$ candidates. We first introduce a practical allocation policy, termed gate + top-$D$ allocation (GTBA), which combines threshold-based admission control with ranking-based selection. We then propose a novel ranking-aware bulk outage loss (RBOL) that provides a differentiable surrogate for the bulk outage event induced by GTBA, explicitly accounting for both gate failures and ranking errors near the selection boundary. An exact reliability analysis is developed, establishing a decomposition of bulk outage probability (BOP), identifying dominant failure mechanisms and deriving an oracle lower bound that characterizes the fundamental performance limit. Extensive simulations under balanced, light and heavy stress regimes demonstrate that RBOL consistently outperforms conventional pointwise losses and baselines, achieving substantial reductions in BOP and remaining significantly closer to the oracle bound across a wide range of operating conditions. These results confirm that set-level ranking-aware training objectives are essential for reliable ML-assisted bulk resource allocation.

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arXiv2026

Energy-Efficient Federated Learning with Relay-Assisted Aggregation in IIoT Networks

This paper presents an energy-efficient transmission framework for federated learning (FL) in industrial Internet of Things (IIoT) environments with strict latency and energy constraints. Machinery subnetworks (SNs) collaboratively train a global model by uploading local updates to an edge server (ES), either directly or via neighboring SNs acting as decode-and-forward relays. To enhance communication efficiency, relays perform partial aggregation before forwarding the models to the ES, significantly reducing overhead and training latency. We analyze the convergence behavior of this relay-assisted FL scheme. To address the inherent energy efficiency (EE) challenges, we decompose the original non-convex optimization problem into sub-problems addressing computation and communication energy separately. An SN grouping algorithm categorizes devices into single-hop and two-hop transmitters based on latency minimization, followed by a relay selection mechanism. To improve FL reliability, we further maximize the number of SNs that meet the roundwise delay constraint, promoting broader participation and improved convergence stability under practical IIoT data distributions. Transmit power levels are then optimized to maximize EE, and a sequential parametric convex approximation (SPCA) method is proposed for joint configuration of system parameters. We further extend the EE formulation to the imperfect channel state information (ICSI). Simulation results demonstrate that the proposed framework significantly enhances convergence speed, reduces outage probability from 10-2 in single-hop to 10-6 and achieves substantial energy savings, with the SPCA approach reducing energy consumption by at least 2x compared to unaggregated cooperation and up to 6x over single-hop transmission.

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arXiv2026

Robust SAC-Enabled UAV-RIS Assisted Secure MISO Systems With Untrusted EH Receivers

Secure downlink transmission in UAV-assisted reconfigurable intelligent surface (RIS)-enabled multiuser MISO systems is challenging due to imperfect channel state information (CSI), untrusted energy-harvesting receivers (UEHRs), and the strong coupling among UAV deployment, transmit power control, and RIS configuration. In this paper, we study a secure UAV-assisted RIS-enabled multiuser MISO system with UEHRs, where a hovering UAV-mounted RIS is jointly optimized in terms of its location, transmit power allocation, and discrete RIS phase shifts. The objective is to maximize the worst-case secrecy energy efficiency (WCSEE) under imperfect CSI and practical discrete phase-shift constraints. The resulting problem is highly nonconvex due to the fractional objective, coupled design variables, discrete phase shifts, and CSI uncertainty. To address these challenges, we propose two complementary approaches. First, a block coordinate descent (BCD) framework combined with successive convex approximation (SCA) is developed to solve a secrecy energy efficiency (SEE) formulation, serving as a structured model-based benchmark. Second, for the more general WCSEE problem, we propose a tailored soft actor-critic (SAC) framework that captures the coupling among variables and avoids repeated iterative optimization. Simulation results show that the proposed SAC method consistently outperforms conventional optimization and deep reinforcement learning (DRL)-based benchmarks, including deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3), while maintaining robustness to CSI uncertainty and stable performance across system configurations.

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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.

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arXiv2026

Deep Reinforcement Learning-Based Dynamic Resource Allocation in Cell-Free Massive MIMO

In this paper, we consider power allocation and antenna activation of cell-free massive multiple-input multiple-output (CFmMIMO) systems. We first derive closed-form expressions for the system spectral efficiency (SE) and energy efficiency (EE) as functions of the power allocation coefficients and the number of active antennas at the access points (APs). Then, we aim to enhance the EE through jointly optimizing antenna activation and power control. This task leads to a non-convex and mixed-integer design problem with high-dimensional design variables. To address this, we propose a novel DRL-based framework, in which the agent learns to map large-scale fading coefficients to AP activation ratio, antenna coefficient, and power coefficient. These coefficients are then employed to determine the number of active antennas per AP and the power factors assigned to users based on closed-form expressions. By optimizing these parameters instead of directly controlling antenna selection and power allocation, the proposed method transforms the intractable optimization into a low-dimensional learning task. Our extensive simulations demonstrate the efficiency and scalability of the proposed scheme. Specifically, in a CFmMIMO system with 40 APs and 20 users, it achieves a 50% EE improvement and 3350 times run time reduction compared to the conventional sequential convex approximation method.

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arXiv2026

Cell-Free Massive MIMO for Joint Communication and Proactive Monitoring

This paper introduces a novel joint communication and proactive monitoring (JCAM) system that simultaneously monitors multiple untrusted links and serves multiple legitimate users. The system leverages a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture, where one subset of access points (APs) is dedicated to receiving signals from untrusted links, while another subset transmits data to legitimate users and jamming signals into the untrusted links. This dual functionality not only ensures reliable communication for legitimate users but also degrades the performance of untrusted links, thereby enhancing monitoring effectiveness. Closed-form expressions for the spectral efficiency (SE) of legitimate users and the monitoring success probability (MSP) are derived under partial zero-forcing (PZF) precoding/combining schemes with imperfect channel state information. Leveraging these expressions, we develop a simple yet effective AP mode assignment strategy that determines which APs perform downlink transmission and jamming, and which APs are dedicated to receiving signals from untrusted links. The objective is to maximize the MSP while satisfying predefined quality-of-service (QoS) requirements for all legitimate users. Numerical results show that the proposed mode assignment strategy significantly outperforms the benchmark, achieving up to a $32\%$ improvement in monitoring performance, while maintaining low computational complexity. Moreover, our proposed JCAM framework provides nearly a six-fold improvement in the minimum MSP over the co-located massive MIMO baseline.

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arXiv2026

Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks

This paper investigates energy-efficient transmission protocols in relay-assisted federated learning (FL) setup within industrial subnetworks, considering latency and power constraints. In the subnetworks, devices collaborate to train a global model by transmitting their local models at the edge-enabled primary access (pAP) directly or via secondary access points (sAPs), which act as relays to optimize the training latency. We begin by formulating the energy efficiency problem for our proposed transmission protocol. Given its non-convex nature, we decompose it to minimize computational and transmission energy separately. First, we introduce an algorithm that categorizes devices into single-hop and two-hop groups to decrease transmission energy and then selects associated sAPs. Subsequently, we optimize the transmit power, aiming to maximize energy efficiency. To that end, we propose a Sequential Parametric Convex Approximation (SPCA) method to configure system parameters jointly. Numerical results demonstrate a significant reduction in outage probability and at least a twofold savings in total energy consumption, together with faster convergence, compared with single-hop transmission.

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arXiv2026

Secure Rate-Splitting and RIS Beamforming with Untrusted Energy Harvesting Receivers

We consider a reconfigurable intelligent surface (RIS)-assisted heterogeneous network comprising legitimate information-harvesting receivers (IHRs) and untrusted energy-harvesting receivers (UEHRs). A multi-antenna base station (BS) transmits confidential information to IHRs while ensuring sufficient energy transfer to UEHRs that may attempt eavesdropping. To enhance physical-layer security, we propose a secure rate-splitting multiple access (RSMA) scheme aided by a UAV-mounted RIS. The objective is to maximize fairness-based secrecy energy efficiency (SEE). Owing to the non-convexity of the formulated problem, we develop an alternating optimization framework that jointly designs the common message allocation, active precoders, and RIS phase shifts under transmit power and energy harvesting constraints, leveraging sequential convex approximation (SCA). Simulation results demonstrate the scalability of the proposed algorithm and its superior SEE performance compared to space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) benchmarks.

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arXiv2026

Availability of Aerial Heterogeneous Networks for Reliable Emergency Communications

We investigate network availability (NA) in aerial heterogeneous networks (AHetNets) for effective emergency rescue, where diverse delay-constrained communication services must be provided to user equipments (UEs) with varying mobility. The heterogeneity in delay constraints and UE mobility introduces resource allocation conflicts and imbalances, which undermine communication reliability and challenge NA. Although unified resource allocation (URA) can mitigate these issues, it remains unclear whether NA can be sustained under such diverse conditions. To address this, we derive expressions for the lower bound (LB) on NA in AHetNets under URA. Our analysis reveals that extended heterogeneity significantly degrades the LB due to resource limitations-even when the heterogeneity stems from additional services under less stringent delay constraints (LSDC) or from UEs with lower mobility. To overcome this degradation, we formulate and solve a joint optimization problem for the number of UEs sharing time-frequency resources ($K$) and pilot length ($ξ$), aiming to enhance the LB by improving spatial, frequency, and temporal resource efficiency. Simulation results validate our analysis and demonstrate that jointly optimizing $K$ and $ξ$ enables AHetNets to achieve the target NA under greater heterogeneity, outperforming existing resource allocation policies.

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