Analysis of One-Bit DAC for RIS-Assisted MU Massive MIMO Systems with Efficient Autoencoder Based Deep Learning

Abstract

This paper proposes an autoencoder-based deep learning approach for multiuser massive multiple-input multiple-output (mMIMO) downlink systems assisted by a reconfigurable intelligent surface (RIS) whose base station is equipped with an antenna array with 1-bit digital-to-analog converters (DACs) to serve multiple user terminals. RIS has introduced today one of the most revolutionary techniques to improve spectrum and energy efficiency for the 6G of wireless networks. First, we present an analytical study on the effects of 1bit DAC on the system under consideration for a Rician fading channel. Then, the transmission system assisted by the proposed RIS design is presented, which allows network operators to control the signal propagation environment. To further improve our system, we propose the deep learning technique to compensate for the signal degradation caused by 1-bit DACs. Numerical simulations demonstrate that the compensation technique considered with the RIS presence achieves competitive performance compared to the existing literature.