Study of Distributed Robust Beamforming with Low-Rank and Cross-Correlation Techniques

TL;DR

This paper introduces a robust distributed beamforming method that uses low-rank and cross-correlation techniques to improve wireless relay network performance under channel errors without costly online optimization.

Contribution

It proposes the LRCC-RDB approach that mitigates channel errors using novel low-rank and cross-correlation techniques, enhancing system robustness and efficiency.

Findings

LRCC-RDB outperforms existing algorithms in simulations.

The method does not require online optimization, reducing computational costs.

Performance analysis confirms robustness against channel errors.

Abstract

In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-to-interference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.