A Bio-Inspired Robust Adaptive Random Search Algorithm for Distributed Beamforming

TL;DR

This paper introduces BioRARSA, a bio-inspired adaptive algorithm for distributed beamforming that converges efficiently and outperforms existing methods by up to 29.8%, with robustness against network dynamics.

Contribution

The paper presents BioRARSA, a novel bio-inspired adaptive random search algorithm that improves convergence speed and robustness in distributed beamforming for wireless networks.

Findings

BioRARSA converges in probability with linear scaling of convergence time.

It outperforms existing schemes by up to 29.8% on average.

The algorithm's stepsize adaptation makes it robust to network dynamics.

Abstract

A bio-inspired robust adaptive random search algorithm (BioRARSA), designed for distributed beamforming for sensor and relay networks, is proposed in this work. It has been shown via a systematic framework that BioRARSA converges in probability and its convergence time scales linearly with the number of distributed transmitters. More importantly, extensive simulation results demonstrate that the proposed BioRARSA outperforms existing adaptive distributed beamforming schemes by as large as 29.8% on average. This increase in performance results from the fact that BioRARSA can adaptively adjust its sampling stepsize via the "swim" behavior inspired by the bacterial foraging mechanism. Hence, the convergence time of BioRARSA is insensitive to the initial sampling stepsize of the algorithm, which makes it robust against the dynamic nature of distributed wireless networks.