Digital Predistortion for Hybrid MIMO Transmitters

TL;DR

This paper introduces a new digital predistortion technique for hybrid MIMO transmitters that improves linearization, reduces nonlinear distortions in the main beam, and is robust to hardware imbalances, outperforming existing methods.

Contribution

A novel DPD processing and learning method for antenna sub-arrays using combined PA signals and decorrelation, enhancing linearization in hybrid beamforming systems.

Findings

Outperforms state-of-the-art single-PA learning techniques.

Robust to high levels of feedback network imbalances.

Array emissions are below single-antenna levels due to DPD and beamforming.

Abstract

This article investigates digital predistortion (DPD) linearization of hybrid beamforming large-scale antenna transmitters. We propose a novel DPD processing and learning technique for an antenna sub-array, which utilizes a combined signal of the individual power amplifier (PA) outputs in conjunction with a decorrelation-based learning rule. In effect, the proposed approach results in minimizing the nonlinear distortions in the direction of the intended receiver. This feature is highly desirable, since emissions in other directions are naturally weak due to beamforming. The proposed parameter learning technique requires only a single observation receiver, and therefore supports simple hardware implementation. It is also shown to clearly outperform the current state-of-the-art technique which utilizes only a single PA for learning. Analysis of the feedback network amplitude and phase imbalances reveals that the technique is robust even to high levels of such imbalances. Finally, we also show that the array system out-of-band emissions are well-behaving in all spatial directions, and essentially below those of the corresponding single-antenna transmitter, due to the combined effects of the DPD and beamforming.