Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems

TL;DR

This paper introduces a neural network-based approach to optimize precoding in massive MIMO systems with non-linear power amplifier distortion, significantly improving energy efficiency over traditional methods.

Contribution

It proposes a novel neural network method to learn precoding that accounts for PA non-linearity, enhancing energy efficiency in massive MIMO systems.

Findings

Neural network precoding outperforms conventional precoders.

Achieves higher energy efficiency than digital pre-distortion.

Effective for third-order polynomial PA models.

Abstract

Massive multiple input multiple output (MIMO) systems are typically designed under the assumption of linear power amplifiers (PAs). However, PAs are typically most energy-efficient when operating close to their saturation point, where they cause non-linear distortion. Moreover, when using conventional precoders, this distortion coherently combines at the user locations, limiting performance. As such, when designing an energy-efficient massive MIMO system, this distortion has to be managed. In this work, we propose the use of a neural network (NN) to learn the mapping between the channel matrix and the precoding matrix, which maximizes the sum rate in the presence of this non-linear distortion. This is done for a third-order polynomial PA model for both the single and multi-user case. By learning this mapping a significant increase in energy efficiency is achieved as compared to conventional precoders and even as compared to perfect digital pre-distortion (DPD), in the saturation regime.