Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities

TL;DR

This paper proposes an autoprecoder-based deep learning method for massive MU-MIMO downlink systems that effectively mitigates PA nonlinearities and multiuser interference with low computational complexity, suitable for energy-efficient large-scale antenna systems.

Contribution

It introduces AP-mMIMO, a novel PA-aware precoding approach using deep learning that jointly addresses nonlinear distortions and interference with reduced complexity.

Findings

Achieves competitive performance with lower complexity.

Effectively compensates for PA nonlinearities.

Suitable for varying channel conditions.

Abstract

This paper introduces a new efficient autoprecoder (AP) based deep learning approach for massive multiple-input multiple-output (mMIMO) downlink systems in which the base station is equipped with a large number of antennas with energy-efficient power amplifiers (PAs) and serves multiple user terminals. We present AP-mMIMO, a new method that jointly eliminates the multiuser interference and compensates the severe nonlinear (NL) PA distortions. Unlike previous works, AP-mMIMO has a low computational complexity, making it suitable for a global energy-efficient system. Specifically, we aim to design the PA-aware precoder and the receive decoder by leveraging the concept of autoprecoder, whereas the end-to-end massive multiuser (MU)-MIMO downlink is designed using a deep neural network (NN). Most importantly, the proposed AP-mMIMO is suited for the varying block fading channel scenario. To deal with such scenarios, we consider a two-stage precoding scheme: 1) a NN-precoder is used to address the PA non-linearities and 2) a linear precoder is used to suppress the multiuser interference. The NN-precoder and the receive decoder are trained off-line and when the channel varies, only the linear precoder changes on-line. This latter is designed by using the widely used zero-forcing precoding scheme or its lowcomplexity version based on matrix polynomials. Numerical simulations show that the proposed AP-mMIMO approach achieves competitive performance with a significantly lower complexity compared to existing literature. Index Terms-multiuser (MU) precoding, massive multipleinput multiple-output (MIMO), energy-efficiency, hardware impairment, power amplifier (PA) nonlinearities, autoprecoder, deep learning, neural network (NN)