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Author

Huy T. Nguyen

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Open access Aug 2026

Scalable RIS-aided hybrid beamforming for mmWave systems enables multiple sub-array architectures: A Geometric Mean Approach

This paper investigates a reconfigurable intelligent surface (RIS)-aided hybrid beamforming (HBF) framework for downlink multi-user millimeter-wave (mmWave) systems operating under practical hardware constraints. Given the severe path-loss disparity and blockage sensitivity inherent in mmWave propagation, RIS-assisted systems often suffer from pronounced user-rate imbalance, a challenge further exacerbated by the restricted spatial degrees of freedom in overlapped sub-array (OSA) hybrid architectures. To explicitly mitigate this disparity, we formulate a hardware-constrained optimization problem to maximize the geometric mean (GM) of user rates, thereby enforcing instantaneous fairness and service reliability. At the base station, an OSA-based HBF architecture is adopted to balance spatial flexibility and RF-chain efficiency, while the RIS operates with discrete phase shifts. Distinctive from conventional sum-rate-driven or idealized fully-digital designs, the proposed framework integrates GM-rate maximization with a manifold-theoretic optimization approach, enabling a realizable mapping from the fully-digital benchmark to a practical hybrid analog-digital structure. A two-block block coordinate descent (BCD) algorithm is developed, wherein the active precoder update admits an exact power-constrained solution and the passive phase optimization is executed on a Riemannian manifold to handle unit-modulus and discrete constraints. Numerical simulations demonstrate that the proposed approach effectively exploits OSA connectivity to achieve robust fairness across users. Specifically, an interference-aware warm-start strategy significantly accelerates convergence and yields an objective value improvement of up to 18.18% compared with existing methods, confirming both mathematical superiority and practical computational efficiency. Moreover, the results validate that low-resolution (e.g., 3-bit) phase shifters are sufficient to approach continuous-phase performance, underscoring the feasibility of the proposed framework for cost- and energy-efficient RIS-assisted mmWave deployments.

H. M. Tran, T. V. Dinh, H. T. Nguyen et al. · 0 citations
Conference Jul 2026

DRL-Based Joint Beamforming and Surface Morphing for FIM-Enabled ISAC Systems

In this paper, we investigate the sum rate maximization problem for integrated sensing and communication (ISAC) systems enabled by a flexible intelligent metasurface (FIM) deployed at the base station. By enabling element movement through surface morphing, the FIM introduces additional spatial degrees of freedom that can reshape the wireless propagation environment. To exploit this capability, we jointly optimize the transmit digital beamforming at the base station and the FIM surface configuration, characterized by the positions of its radiating elements, subject to transmit power constraints and minimum sensing beam gain requirements. Since the resulting problem is highly non-convex due to the strong coupling between the beamforming vectors and the element positions, a deep reinforcement learning (DRL)-based algorithm is proposed to efficiently obtain high-quality solutions. Numerical results demonstrate that the proposed framework significantly improves the achievable sum rate while satisfying the sensing performance constraints.

Ho-Ang T. Hung, H. H. Nguyen, Huy T. Nguyen et al. · 0 citations

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