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).
@article{arxiv.2205.05158,
title = {Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink},
author = {Yibo Wu and Ulf Gustavsson and Mikko Valkama and Alexandre Graell i Amat and Henk Wymeersch},
journal= {arXiv preprint arXiv:2205.05158},
year = {2022}
}