Sub-Array Selection in Full-Duplex Massive MIMO for Enhanced Self-Interference Suppression

TL;DR

This paper introduces a hybrid beamforming and sub-array selection approach in full-duplex massive MIMO systems, achieving significant self-interference suppression by optimizing beam directivity and sub-array configurations.

Contribution

It presents a novel joint design of RF beamforming and sub-array selection using swarm intelligence to minimize self-interference in full-duplex mMIMO systems.

Findings

Achieves up to 78 dB self-interference suppression

Proposes a swarm intelligence algorithm for nonconvex optimization

Demonstrates effective SI mitigation based on measured channel data

Abstract

This study considers a novel full-duplex (FD) massive multiple-input multiple-output (mMIMO) system using hybrid beamforming (HBF) architecture, which allows for simultaneous uplink (UL) and downlink (DL) transmission over the same frequency band. Particularly, our objective is to mitigate the strong self-interference (SI) solely on the design of UL and DL RF beamforming stages jointly with sub-array selection (SAS) for transmit (Tx) and receive (Rx) sub-arrays at base station (BS). Based on the measured SI channel in an anechoic chamber, we propose a min-SI beamforming scheme with SAS, which applies perturbations to the beam directivity to enhance SI suppression in UL and DL beam directions. To solve this challenging nonconvex optimization problem, we propose a swarm intelligence-based algorithmic solution to find the optimal perturbations as well as the Tx and Rx sub-arrays to minimize SI subject to the directivity degradation constraints for the UL and DL beams. The results show that the proposed min-SI BF scheme can achieve SI suppression as high as 78 dB in FD mMIMO systems.