Base station (BS) architectures for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems are equipped with hundreds of antennas to serve tens of users on the same time-frequency channel. The immense number of BS antennas incurs high system costs, power, and interconnect bandwidth. To circumvent these obstacles, sophisticated MU precoding algorithms that enable the use of 1-bit DACs have been proposed. Many of these precoders feature parameters that are, traditionally, tuned manually to optimize their performance. We propose to use deep-learning tools to automatically tune such 1-bit precoders. Specifically, we optimize the biConvex 1-bit PrecOding (C2PO) algorithm using neural networks. Compared to the original C2PO algorithm, our neural-network optimized (NNO-)C2PO achieves the same error-rate performance at 2× lower complexity. Moreover, by training NNO-C2PO for different channel models, we show that 1-bit precoding can be made robust to vastly changing propagation conditions.
@article{arxiv.1903.03718,
title = {Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO},
author = {Alexios Balatsoukas-Stimming and Oscar Castañeda and Sven Jacobsson and Giuseppe Durisi and Christoph Studer},
journal= {arXiv preprint arXiv:1903.03718},
year = {2019}
}