Stability analysis of the consensus-based distributed LMS algorithm

Ioannis D. Schizas, Gonzalo Mateos, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

We deal with consensus-based online estimation and tracking of (non-) stationary signals using ad hoc wireless sensor networks (WSNs). A distributed (D-) least-mean square (LMS) like algorithm is developed, which offers simplicity and flexibility, while it solely relies on single-hop communications among sensors. Starting from a pertinent squared-error cost, we apply the alternating-direction method of multipliers to minimize it in a distributed fashion; and utilize stochastic approximation tools to eliminate the need for a complete statistical characterization of the processes of interest. By resorting to stochastic averaging and perturbed Lyapunov techniques, we further establish that local estimates are exponentially convergent to the true parameter of interest when observations are noise free and linearly related to it. This convergence result is necessary for bounding the estimation error in the presence of noise, and holds not only when regressors are white across time but even when they exhibit temporal correlations. Numerical tests confirm the merits of the novel D-LMS algorithm and its stability analysis.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages3289-3292
Number of pages4
DOIs
StatePublished - Sep 16 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/TerritoryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Adaptive signal processing
  • Distributed algorithms
  • Distributed estimation

Fingerprint

Dive into the research topics of 'Stability analysis of the consensus-based distributed LMS algorithm'. Together they form a unique fingerprint.

Cite this