# Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO

## TL;DR

This paper introduces a neural network-based approach to optimize 1-bit precoding in massive MU-MIMO systems, significantly reducing complexity while maintaining error-rate performance and enhancing robustness across different channel conditions.

## Contribution

It presents a novel neural network optimization of the C2PO algorithm, enabling automatic tuning and improved efficiency for 1-bit precoding in massive MIMO systems.

## Key findings

- Achieves same error-rate at half the complexity of original C2PO.
- Demonstrates robustness of 1-bit precoding across various channel models.
- Neural network tuning enhances adaptability to changing propagation conditions.

## Abstract

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 $\bf 2\boldsymbol\times$ 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.