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

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 ($\text{textsc}P-PGNBB$) 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.