[Globecom 2025: Shivani] Parametric Channel Estimation for Near-Field RIS and Data Demodulation скачать в хорошем качестве

[Globecom 2025: Shivani] Parametric Channel Estimation for Near-Field RIS and Data Demodulation

[This work was presented at the IEEE Global Communications Conference (Globecom), Taipei, Taiwan in Dec. 2025] The steering vector for the channel between a reconfigurable intelligent surface (RIS) and a user depends on the distance and the azimuth of the user relative to the RIS when the user lies in the near-field of the RIS. Hence, techniques for estimating the RIS-user channel’s gain differ from those for conventional far-field estimation. This estimate is necessary to configure the RIS phases. We study a low training overhead parametric maximum likelihood scheme that estimates the directional cosine, distance, and complex gain to reconstruct the RIS-user channel. We present a novel linear minimum mean square error (LMMSE) estimator for the access point (AP)-RIS-user effective channel gain. Estimating this channel is necessary to demodulate data. Our estimator is based on novel expressions for the mean and variance of the effective channel gain. It accounts for the impact of errors in estimating the RIS-user channel, which leads to an imperfect RIS phase configuration, and the noise while estimating the effective channel gain. We present novel expressions of the Cramer-Rao lower bound of the mean square error (MSE) of the estimated channel parameters and the RIS-user channel gain. Our approach achieves a lower MSE and a lower symbol error probability than several benchmarking schemes. Fewer pilots than the number of RIS elements are required to achieve a given NMSE.