Efficient Neural Network DPD Architecture for Hybrid Beamforming mMIMO

Author:

Muškatirović-Zekić Tamara12,Nešković Nataša1,Budimir Djuradj13

Affiliation:

1. School of Electrical Engineering, University of Belgrade, 11120 Belgrade, Serbia

2. Regulatory Agency for Electronic Communications and Postal Services, 11103 Belgrade, Serbia

3. Wireless Communications Research Group, University of Westminster, London W1B 2HW, UK

Abstract

This paper presents several different Neural Network based DPD architectures for hybrid beamforming (HBF) mMIMO applications. They are formulated, tested and compared based on their ability to compensate nonlinear distortion of power amplifiers in a single user (SU) and multiuser (MU) Fully-Connected (FC) HBF mMIMO transmitters. The proof-of-concept is provided with a 64 × 64 FC HBF mMIMO system, with 2 RF chains. The complexity of DPD solution is reduced by using a single Real-Valued Time-Delay Neural Network with two hidden layers (RVTDNN2L) instead of using as many different DPD blocks as there are RF chains in the HBF mMIMO transmitter and it is shown that the proposed architecture better compensates nonlinear distortion compared to the traditional memory polynomial DPD. Two RVTDNN2L DPD architectures are developed and tested for linearization of MU FC HBF mMIMO systems, and it is also shown that the proposed RVTDNN2L DPD architecture efficiently linearizes MU FC HBF mMIMO transmitters in terms of Normalized Mean-Squared Error (NMSE) and Error Vector Magnitude (EVM).

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Reference32 articles.

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