Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink
Paper in proceeding, 2022

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).

Author

Yibo Wu

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Ulf Gustavsson

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Mikko Valkama

University of Tampere

Alexandre Graell I Amat

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

GLOBECOM - IEEE Global Telecommunications Conference

579-584
9781665435406 (ISBN)

2022 IEEE Global Communications Conference, GLOBECOM 2022
Rio de Janeiro, Brazil,

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/GLOBECOM48099.2022.10001382

More information

Latest update

10/25/2023