Energy-Efficient Deterministic Adaptive Beamforming Algorithms for Distributed Sensor/Relay Networks

TL;DR

This paper introduces energy-efficient deterministic adaptive beamforming algorithms for distributed sensor and relay networks, demonstrating their convergence, robustness, and superior performance over random algorithms through analysis and simulations.

Contribution

The paper proposes new deterministic beamforming algorithms, including hybrid approaches, with proven convergence and improved robustness and efficiency in sensor/relay networks.

Findings

Superior convergence performance of proposed algorithms.

Enhanced robustness against node removal and channel variations.

Hybrid-QESA achieves best performance in time-varying channels.

Abstract

In this study, energy-efficient deterministic adaptive beamforming algorithms are proposed for distributed sensor/relay networks. Specifically, DBSA, D-QESA, D-QESA-E, and a hybrid algorithm, hybrid-QESA, that combines the benefits of both deterministic and random adaptive beamforming algorithms, are proposed. Rigorous convergence analyses are provided for all our proposed algorithms and convergence to the global optimal solution is shown for all our proposed algorithms. Through extensive numerical simulations, we demonstrate that superior performance is achieved by our proposed DBSA and D-QESA over random adaptive beamforming algorithms for static channels. Surprisingly, D-QESA is also more robust against random node removal than random adaptive beamforming algorithms. For time-varying channels, hybrid-QESA indeed achieves the best performance since it combines the benefits of both types of adaptive beamforming algorithms. In summary, our proposed deterministic algorithms demonstrate superior performance both in terms of convergence time and robustness against channel and network uncertainties.