arXiv subjects

Elhadj Moustapha Diallo

Publications and source records attributed to Elhadj Moustapha Diallo.

5 recordsLinked to original sources

arXiv2026

Chance-Constrained Secrecy Optimization in Hybrid RIS-Empowered and UAV-Assisted Networks

This paper considers a hybrid reconfigurable environment comprising a UAV-mounted reflecting RIS, an outdoor STAR-RIS enabling simultaneous transmission and reflection, and an indoor holographic RIS (H-RIS), jointly enhancing secure downlink communication for indoor and outdoor users. The system operates under user mobility, dynamic blockages, colluding idle and active eavesdroppers, and transceiver and surface hardware impairments. A 3GPP and ITU-compliant stochastic channel model is developed, capturing mobility-induced covariance evolution, outdoor-indoor penetration losses, and distortion-aware noise due to practical EVM-based impairments. We aim to minimize the aggregate secrecy-outage probability subject to secrecy-rate constraints, QoS requirements, power limitations, and statistical CSI uncertainty. The resulting problem contains coupled secrecy and QoS chance constraints and nonlinear interactions among the BS beamforming vectors, multi-surface phase coefficients, and UAV position. To handle these difficulties, we derive rigorous Bernstein-type deterministic approximations for all chance constraints, yielding a distributionally robust reformulation. Building on this, we propose an alternating optimization framework that employs successive convex approximation (SCA) to convexify each block and solve the BS beamforming, RIS, STAR-RIS, H-RIS configuration, and UAV placement subproblems efficiently. The proposed algorithm is shown to monotonically decrease a smooth surrogate of the secrecy-outage cost and converge to a stationary point of the robustified problem. Simulations based on 3GPP TR 38.901, TR 36.873, and ITU-R P.2109 demonstrate that integrating UAV-RIS, STAR-RIS, and H-RIS significantly reduces secrecy-outage probability compared with benchmark schemes and provides strong robustness to channel uncertainty, blockages, colluding eavesdroppers, and hardware impairments.

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arXiv2025

Interference Mitigation in STAR-RIS-Aided Multi-User Networks with Statistical CSI

In this paper, we investigate real-time interference mitigation in multiuser wireless networks assisted by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). Unlike conventional methods that rely on instantaneous channel state information (CSI), we consider a practical scenario where only statistical CSI is available, and the STAR-RIS phase shifts are impaired by random phase errors modeled via the Von Mises distribution. To tackle the resulting nonconvex optimization problem induced by unit-modulus constraints and stochastic interference, we derive a closed-form approximation of the effective channel matrix using statistical expectations. We then reformulate the interference minimization problem as an unconstrained optimization over a Riemannian manifold and propose a conjugate gradient algorithm tailored to the complex circle manifold. The proposed solution enables efficient real-time computation of optimal phase shifts while accounting for hardware imperfections and limited CSI. Simulation results confirm that our method significantly suppresses inter-user interference and achieves superior SINR performance and convergence speed compared to conventional baselines.

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arXiv2024

Adaptive Soft Actor-Critic Framework for RIS-Assisted and UAV-Aided Communication

In this work, we explore UAV-assisted reconfigurable intelligent surface (RIS) technology to enhance downlink communications in wireless networks. By integrating RIS on both UAVs and ground infrastructure, we aim to boost network coverage, fairness, and resilience against challenges such as UAV jitter. To maximize the minimum achievable user rate, we formulate a joint optimization problem involving beamforming, phase shifts, and UAV trajectory. To address this problem, we propose an adaptive soft actor-critic (ASAC) framework. In this approach, agents are built using adaptive sparse transformers with attentive feature refinement (ASTAFER), enabling dynamic feature processing that adapts to real-time network conditions. The ASAC model learns optimal solutions to the coupled subproblems in real time, delivering an end-to-end solution without relying on iterative or relaxation-based methods. Simulation results demonstrate that our ASAC-based approach achieves better performance compared to the conventional SAC. This makes it a robust, adaptable solution for real-time, fair, and efficient downlink communication in UAV-RIS networks.

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arXiv2024

Generative Model for Joint Resource Management in Multi-Cell Multi-Carrier NOMA Networks

In this work, we design a generative artificial intelligence (GAI) -based framework for joint resource allocation, beamforming, and power allocation in multi-cell multi-carrier non-orthogonal multiple access (NOMA) networks. We formulate the proposed problem as sum rate maximization problem. Next, we design a novel multi-task transformer (MTT) framework to handle the problem in real-time. To provide the necessary training set, we consider simplified but powerful mathematical techniques from the literature. Then, we train and test the proposed MTT. We perform simulation to evaluate the efficiency of the proposed MTT and compare its performance with the mathematical baseline.

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arXiv2024

Optimizing Wireless Networks with Deep Unfolding: Comparative Study on Two Deep Unfolding Mechanisms

In this work, we conduct a comparative study on two deep unfolding mechanisms to efficiently perform power control in the next generation wireless networks. The power control problem is formulated as energy efficiency over multiple interference links. The problem is nonconvex. We employ fractional programming transformation to design two solutions for the problem. The first solution is a numerical solution while the second solution is a closed-form solution. Based on the first solution, we design a semi-unfolding deep learning model where we combine the domain knowledge of the wireless communications and the recent advances in the data-driven deep learning. Moreover, on the highlights of the closed-form solution, fully deep unfolded deep learning model is designed in which we fully leveraged the expressive closed-form power control solution and deep learning advances. In the simulation results, we compare the performance of the proposed deep learning models and the iterative solutions in terms of accuracy and inference speed to show their suitability for the real-time application in next generation networks.

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