Joint Training of the Superimposed Direct and Reflected Links in Reconfigurable Intelligent Surface Assisted Multiuser Communications

Abstract

In reconfigurable intelligent surface (RIS)-assisted systems the acquisition of channel state information and the optimization of reflecting coefficients constitute major design challenges. In this paper, a novel channel training-based protocol is proposed, which is capable of striking a flexible trade-off between performance, pilot overhead and complexity. More specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first of all</i> , we conceive a holistic protocol that intrinsically amalgamates the existing channel estimation and passive beamforming optimization for creating a new unified scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Secondly</i> , we propose a new channel training framework. In contrast to the conventional channel estimation arrangements, our new framework divides the training phase into several periods and has the compelling benefit of directly estimating the superimposed end-to-end channel instead of separately estimating the direct BS-user and reflected RIS links, which would not lend itself to near-instantaneous reconfiguration in the face of high-Doppler mobility. As a result, the RIS reflecting coefficients are optimized by comparing the objective function values over multiple training periods, which leads to optimal performance, despite its reduced complexity as well as reduced signaling and pilot overhead. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Thirdly</i> , we analyze the theoretical performance of both the channel estimation-based protocol and the channel training-based protocol in the presence of channel estimation errors. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Finally</i> , our theoretical analysis is confirmed by numerical simulations. In particular, the simulation results demonstrate that our channel training-based protocol is more competitive than the channel estimation-based protocol in the presence of channel estimation errors.