Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumption in massive MU-MIMO, low-resolution digital-to-analog converters (DAC) for each antenna and radio frequency (RF) chain in downlink transmission is used, which brings challenges for precoding design. To circumvent these obstacles, we develop a model-driven deep learning (DL) network for massive MU-MIMO with finite-alphabet precoding in this article. The architecture of the network is specially designed by unfolding an iterative algorithm. Compared with the traditional state-of-the-art techniques, the proposed DL-based precoder shows significant advantages in performance, complexity, and robustness to channel estimation error under Rayleigh fading channel.
@article{arxiv.2006.15495,
title = {Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding},
author = {Hengtao He and Mengjiao Zhang and Shi Jin and Chao-Kai Wen and Geoffrey Ye Li},
journal= {arXiv preprint arXiv:2006.15495},
year = {2020}
}
Comments
5 pages, 5 figures, accepted by IEEE comm. letters. The code will be available at https://github.com/hehengtao/IDE2-Net