Analysis one-bit DAC for MU massive MIMO downlink via efficient autoencoder based deep learning

Abstract

Multi-user (MU) massive multiple input multiple output (mMIMO) is considered a potential technology for fifth generation (5G) and sixth-generation (6G) wireless systems. The presence of the antenna arrays at the base station level to communicate with the users or to serve tens of single antenna users leads to excessively high system costs and power consumption. The deployment 1-bit digital-to-analogue converters (DACs) in the base station can solve these problems. This paper starts by presenting an analytical study centered on the effects of 1-bit DACs on the system envisaged for a Rayleigh-type fading channel. Compact-form expressions are derived for the symbol error rate. Afterwards, an efficient end-to-end deep learning technique to compensate for the joint effect of 1-bit DAC and imperfect channel state information in downlink mMIMO systems. Moreover, to improve the performance of the considered system, a DAC mixed architecture is proposed, where a number of antennas use 1 bit DACs while the others do not. The simulations results showed the improvement in transmission quality of the downlink of the MU-mMIMO system in the presence of hardware imperfections using the considered end-to-end compensation technique.