Channel Estimation for Intelligent Reflecting Surface-Aided Communication Systems with One-bit ADCs

Abstract

Intelligent reflecting surfaces (IRSs) have been regarded as promising enablers for future wireless communications thanks to their ability to customize favorable propagation environments. Meanwhile, the solution of large-scale antenna arrays with low-resolution analog-to-digital converters (ADCs), is supposed to achieve a good performance-complexity trade-off. In this paper, we investigate the channel estimation issue of IRS-aided systems with one-bit ADCs. By utilizing the Bussgang decomposition, we reformulate the non-linear one-bit quantization operation as a statistically equivalent linear model and propose a linear minimum mean square error (LMMSE) channel estimator. Simulation results reveal that the proposed LMMSE estimator can effectively reduce the impact of the quantization distortion, and therefore significantly outperforms the conventional least square estimator.