Individual Channel Estimation for RIS-aided Communication Systems - A General Framework

We propose new pilot transmission protocols for acquiring channel state information (CSI) of individual reconfigurable intelligent surface (RIS) assisted channels. Our approach addresses the challenge of individual CSI acquisition when the RIS lacks sensing and signal processing capabilities. We use monostatic and bistatic full-duplex base stations (BSs) and exploit the reciprocity of the uplink and downlink channels to design channel estimation algorithms based on both unstructured and geometric channel models. Specifically, for unstructured channel models, we develop two different channel estimation algorithms that provide high accuracy and low pilot overhead, respectively, depending on the type of full-duplex BS used. Moreover, a unified estimation framework is proposed to determine the CSI based on geometric channel models for both types of full-duplex BSs. For the angle estimation required as part of the proposed framework, we further develop a high-precision algorithm based on atomic norm minimization (ANM) and a low-complexity algorithm based on orthogonal matching pursuit (OMP). Simulation results reveal that the proposed algorithms are superior to existing methods in terms of estimation accuracy, complexity, and pilot overhead.