Learning-Assisted Beamforming Optimization for Hybrid XL-RIS-Aided Communication
Abstract
Hybrid active-passive extremely large-scale reconfigurable intelligent surface (XL-RIS) architectures mitigate the double-hop path loss of passive RIS while enabling near-field spatial energy focusing, but impose a computationally intensive mixed discrete-continuous beamforming design problem under strict radio-frequency (RF) power constraints. This paper investigates a hybrid XL-RIS-assisted downlink multi-user multiple-input single-output (MU-MISO) system in the near-field regime. The joint optimization of the base station (BS) precoder, discrete XL-RIS phase shifts, and active-element amplification gains is addressed via a discrete coordinate ascent (DCA)-based alternating optimization (AO) framework, in which discrete variables are updated through local index-domain searches and the BS precoder is derived in closed form via a noise-aware minimum mean-square error (MMSE) criterion. To reduce the per-sample computational cost for XL-RIS apertures, a deep unfolding (DU) framework is proposed as a low-complexity alternative, unrolling a fixed number of Riemannian gradient ascent steps on the sum-rate objective and recovering the discrete phase configuration via quantized phasor projection, with one learnable step size per layer trained in a self-supervised manner. Numerical results under near-field spherical-wave channel models confirm that the DU framework achieves sum-rate performance close to the AO scheme with substantially lower and nearly constant runtime, and that both schemes consistently outperform manifold optimization under discrete hardware constraints.
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BibTeX
@misc{abdulrazak2026learningassisted,
title = {{Learning-Assisted Beamforming Optimization for Hybrid XL-RIS-Aided Communication}},
author = {Awuni Mohammed Abdul-Razak and Kyungchun Lee},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.10569},
eprint = {2611.10569},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.10569},
}
RIS
TY - UNPB
TI - Learning-Assisted Beamforming Optimization for Hybrid XL-RIS-Aided Communication
AU - Awuni Mohammed Abdul-Razak
AU - Kyungchun Lee
PY - 2026
DA - 2026/11/07
PB - arXiv
DO - 10.48550/arxiv.2611.10569
UR - https://arxiv.org/abs/2611.10569
AB - Hybrid active-passive extremely large-scale reconfigurable intelligent surface (XL-RIS) architectures mitigate the double-hop path loss of passive RIS while enabling near-field spatial energy focusing, but impose a computationally intensive mixed discrete-continuous beamforming design problem under strict radio-frequency (RF) power constraints. This paper investigates a hybrid XL-RIS-assisted downlink multi-user multiple-input single-output (MU-MISO) system in the near-field regime. The joint optimization of the base station (BS) precoder, discrete XL-RIS phase shifts, and active-element amplification gains is addressed via a discrete coordinate ascent (DCA)-based alternating optimization (AO) framework, in which discrete variables are updated through local index-domain searches and the BS precoder is derived in closed form via a noise-aware minimum mean-square error (MMSE) criterion. To reduce the per-sample computational cost for XL-RIS apertures, a deep unfolding (DU) framework is proposed as a low-complexity alternative, unrolling a fixed number of Riemannian gradient ascent steps on the sum-rate objective and recovering the discrete phase configuration via quantized phasor projection, with one learnable step size per layer trained in a self-supervised manner. Numerical results under near-field spherical-wave channel models confirm that the DU framework achieves sum-rate performance close to the AO scheme with substantially lower and nearly constant runtime, and that both schemes consistently outperform manifold optimization under discrete hardware constraints.
ER -