Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink

TL;DR

This paper introduces a frequency-domain CNN-based digital predistortion method for massive MU-MIMO-OFDM downlink, significantly reducing computational complexity while maintaining performance.

Contribution

It proposes a novel CNN-based DPD approach that operates in the frequency domain before precoding, reducing complexity in massive MIMO systems.

Findings

Large complexity savings with increasing BS antennas

Small power increase needed to maintain SER

Effective linearization of PAs in simulations

Abstract

Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (PAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).