Reducing Complexity of Data-Aided Channel Estimation in RIS-Assisted Communications

Abstract

We consider the data-aided channel estimation (CE) problem in a reconfigurable intelligent surface (RIS)-assisted wireless communication system, where the channel and information symbols are estimated jointly during the CE phase, differently from pure pilot-aided methods. We propose a two-stage semi-blind receiver that jointly estimates the combined channel and the data symbols, followed by channel decoupling. To this end, we derive a new modeling framework whose first stage recasts the received signal to allow for the joint estimation of the combined channel and transmitted symbols. In the second stage, channel decoupling is easily achieved via Khatri-Rao factorization, yielding a refined channel estimate. Our solution yields accurate estimates of the cascaded channel at lower computational complexity. Simulation results reveal a similar performance of the proposed method to that of the competitor while providing a substantially reduced computational cost.