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Azam Khalili

Azam Khalili

Academic rank: Associate Professor
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Education: PhD.
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Faculty: Technical Engineering
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Research

Title
Steady-State Analysis of Diffusion LMS Adaptive Networks With Noisy Links
Type
JournalPaper
Keywords
Adaptive networks, distributed estimation, energy conservation, noisy links
Year
2012
Journal IEEE TRANSACTIONS ON SIGNAL PROCESSING
DOI
Researchers Azam Khalili ، Amir Rastegarnia

Abstract

In this correspondence, we analyze the effects of noisy links on the steady-state performance of diffusion least-mean-square (LMS) adaptive networks. Using the established weighted spatial-temporal energy conservation argument, we derive a variance relation which contains moments that represent the effects of noisy links. We evaluate these moments and derive closed-form expressions for the mean-square deviation (MSD), excess mean-square error (EMSE) and mean-square error (MSE) to explain the steady-state performance at each individual node. The derived expressions, supported by simulations, reveal that unlike the ideal link case, the steady-state MSD, EMSE, and MSE curves are not monotonically increasing functions of the step-size parameter when links are noisy. Moreover, the diffusion LMS adaptive network does not diverge due to noisy links