Digital Predistortion for Fully-Connected Hybrid Beamforming Massive MIMO Transmitters Under Dynamic Directions

DOI number:10.1109/TVT.2024.3444028

Affiliation of Author(s):中国科学技术大学信息学院

Teaching and Research Group:电子工程与信息科学系

Journal:IEEE Transactions on Vehicular Technology

Funded by:国家自然科学基金 NSFC 62371436

Key Words:Digital predistortion (DPD), dynamic model, fully-connected hybrid beamforming (FC-HBF) transmitters, power amplifier (PA)

Abstract:In practical applications, the main beam direction of massive multi-input multi-output (mMIMO) transmitters varies with the user’s location. However, different directions will result in different distortions in mMIMO transmitters, and this poses a big challenge to digital pre-distortion (DPD). This paper first analyzes the relationship between direction configuration and main beam distortion in the fully-connected hybrid beamforming (FC-HBF) transmitters. In order to transmit the signal correctly under dynamic directions, two DPD models using heterogeneous
neural networks (HNNs) based on vector decomposition (VD)
and block-oriented (BO) structure, called HNN-VD and HNN-BO, respectively, have been proposed. The direction information
is used directly as the input of HNNs. Then, a gating-based
neural network model is proposed called direction adaptive
neural network (DANN), which is composed of a gating network
and a backbone network, and its core idea is to dynamically
adjust the hidden layer features of the backbone network by
utilizing direction information through the gating network. The
simulation indicates that all of the proposed models exhibit
good performance, but DANN has the best performance and
low complexity among models, which provides a good digital
pre-distortion scheme for FC-HBF transmitters under dynamic
directions.

First Author:Junsen Wang (王俊森)

Co-author:Chengye Jiang,Renlong Han,Guichen Yang,Qianqian Zhang,Hao Chang

Indexed by:Journal paper

Correspondence Author:Falin Liu

Document Code:10.1109/TVT.2024.3444028

Discipline:Engineering

Document Type:J

Volume:73

Issue:12

Page Number:19156-19168

Date of Publication:2024-12-20

Included Journals:SCI、EI