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arXiv · 2610.27972

RIS Configuration with Low-Overhead Measurements via Physics-Guided Neural Networks

Son Dinh-Van·Matthew D. Higgins
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Abstract

This paper studies reconfigurable intelligent surface (RIS) configuration for a single-input single-output (SISO) communication link using a two-stage estimate-then-optimize procedure. Within this framework, two common approaches to the estimation stage are pilot-based channel estimation and received signal strength (RSS)-based channel estimation. Our hardware experiments reveal substantial inter-block phase evolution consistent with residual CFO, while simulations show that such phase rotations can severely degrade pilot-based channel estimation when left uncompensated. In contrast, RSS-based approaches are less affected by these phase rotations, although they often require a large number of measurements to achieve a near-optimal received signal-to-noise ratio (SNR). To address this measurement requirement, we propose parametric physics-guided neural blind beamforming (\textsc{P-PGNBB}) for RSS-based channel estimation. Simulations and hardware experiments confirm that the proposed method achieves a received SNR close to that obtained with perfect channel state information (CSI) and comparable to existing methods, while requiring substantially fewer RSS measurements.

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Son Dinh-Van, Matthew D. Higgins. 2026-10-20. RIS Configuration with Low-Overhead Measurements via Physics-Guided Neural Networks. https://arxiv.org/abs/2610.27972

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