Closed-form MSE performance of the distributed LMS algorithm

Gonzalo Mateos, Ioannis D. Schizas, Georgios B. Giannakis

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

5 Scopus citations

Abstract

Mean-square error (MSE) performance analysis is conducted for a novel distributed least-mean square (D-LMS) algorithm, which is based on consensus, in-network, adaptive estimation using wireless sensor networks (WSNs). For sensor observations that are linearly related to the time-invariant parameter of interest and independent Gaussian data, exact closed-form expressions are derived for the global and sensor-level MSE evolution and steady-state limiting values. Tracking performance is also investigated when the true parameter adheres to a random-walk model. Remarkably, for small step-sizes the results accurately extend to the pragmatic setup whereby sensors acquire temporally-correlated (non-)Gaussian data.

Original languageEnglish (US)
Title of host publication2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings
Pages66-71
Number of pages6
DOIs
StatePublished - Apr 8 2009
Event2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009 - Marco Island, FL, United States
Duration: Jan 4 2009Jan 7 2009

Publication series

Name2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings

Other

Other2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009
Country/TerritoryUnited States
CityMarco Island, FL
Period1/4/091/7/09

Keywords

  • Distributed estimation
  • LMS algorithm
  • Performance analysis
  • Wireless sensor networks

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