  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).
