Time vs. Frequency Domain DPD for Massive MIMO: Methods and Performance Analysis
Journal article, 2025

The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the nonlinear power amplifiers (PAs). While the complexity for conventional time domain (TD) DPD scales with the number of PAs, frequency domain (FD) DPD has a complexity scaling with the number of user equipments (UEs). In this work, we provide a comprehensive analysis of different state-of-the-art TD and FD-DPD schemes in terms of complexity and linearization performance in both rich scattering and line-of-sight (LOS) channels and with antenna crosstalk. We propose a novel low-complexity FD convolutional neural network (CNN) DPD. We also propose a learning algorithm for any FD-DPDs with differentiable structure. The analysis shows that FD-DPD, particularly the proposed FD CNN, is preferable in LOS scenarios with few users, due to the favorable trade-off between complexity and linearization performance. On the other hand, in scenarios with more users or isotropic scattering channels, significant intermodulation distortions among UEs degrade FD-DPD performance, making TD-DPD more suitable. The proposed learning algorithm allows FD-DPDs to outperform TD-DPD optimized by indirect learning architecture under antenna crosstalk.

time domain (TD)

antenna crosstalk

Massive MIMO

frequency domain (FD)

digital predistortion (DPD)

neural networks (NNs)

power amplifiers (PAs)

deep learning

Author

Yibo Wu

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Ulf Gustavsson

Ericsson

M. 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

IEEE Transactions on Wireless Communications

15361276 (ISSN) 15582248 (eISSN)

Vol. In Press

Subject Categories (SSIF 2025)

Communication Systems

Telecommunications

Signal Processing

DOI

10.1109/TWC.2025.3541184

More information

Latest update

3/12/2025