A General Proof of Convergence for Adaptive Distributed Beamforming Schemes

TL;DR

This paper provides a general proof of convergence for adaptive distributed beamforming schemes by reformulating them as local random search algorithms, applicable to various objective functions and asynchronous updates.

Contribution

It introduces a unified convergence analysis framework for adaptive distributed beamforming schemes, including asynchronous updates, under broad conditions.

Findings

Convergence in probability and in mean is proven under specified conditions.

The framework applies to any objective function with all local maxima being global.

Simulations validate the theoretical convergence results.

Abstract

This work focuses on the convergence analysis of adaptive distributed beamforming schemes that can be reformulated as local random search algorithms via a random search framework. Once reformulated as local random search algorithms, it is proved that under two sufficient conditions: a) the objective function of the algorithm is continuous and all its local maxima are global maxima, and b) the origin is an interior point within the range of the considered transformation of the random perturbation, the corresponding adaptive distributed beamforming schemes converge both in probability and in mean. This proof of convergence is general since it can be applied to analyze randomized adaptive distributed beamforming schemes with any type of objective functions and probability measures as long as both the sufficient conditions are satisfied. Further, this framework can be generalized to analyze an asynchronous scheme where distributed transmitters can only update their beamforming coefficients asynchronously. Simulation results are also provided to validate our analyses.