On the Robust Design of Adaptive Distributed Beamforming for Wireless Sensor/Relay Networks

• Published in: IEEE transactions on signal processing Vol. 62; no. 13; pp. 3429 - 3441
• Main Authors: Chia-Shiang Tseng, Denis, Juwendo, Che Lin
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive systems; Algorithms; Applied sciences; Array signal processing; Beamforming; Channels; convergence analysis; Design engineering; Detection, estimation, filtering, equalization, prediction; distributed algorithms; Economic models; Exact sciences and technology; feedback communications; Information, signal and communications theory; Mathematical analysis; Network topologies; Networks; Noise measurement; Receivers; robust design; Robustness; Signal and communications theory; Signal processing algorithms; Signal, noise; Telecommunications and information theory; Transmitters; Uncertainty; Relay; Wireless telecommunication; Network architecture; Feedback regulation; Adaptive method; Time variation; Beam forming; Remote sensing; Topological structure; Biomimetics; Robustness; Signal detection; Time variable channel; Target tracking; feedback communications; Stochastic stability; Beamforming; convergence analysis; Volume; distributed algorithms; Distributed algorithm; Analytical method; Signal processing; Wireless network; Numerical simulation; robust design; Metric; Sensor array
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2014.2327588

A considerable volume of research into adaptive schemes for transmit beamforming in distributed networks has emerged in recent years. A noise-free received signal strength (RSS) measurement and a static environment were often considered. However, in practical scenarios, system uncertainties often arise, which may lead to the aforementioned idealistic assumptions failing. In this paper, we focus on the robust design of distributed beamforming and proposed a systematic analytical framework for the convergence of a general set of adaptive schemes under the condition that the RSS at the receiver is corrupted by noise. Furthermore, we presented theoretical analysis using stochastic stability to demonstrate the tracking capability of the general adaptive schemes when channels are subject to fast time variations. We defined a set of robustness criteria that can be used as comparison metrics for existing adaptive schemes, under time-varying channels and time-varying network topologies. Through the utilization of the proposed analytical frameworks and metrics, we developed a bio-inspired scheme, BioRARSA2, which possess significantly superior robustness with respect to environmental variations and system uncertainties. The improved robustness of the proposed algorithm was validated further through extensive numerical simulations.