Distributed averaging algorithms resilient to communication noise and dropouts

Jing Wang, Nicola Elia

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

In this paper, we consider the problem of distributed average computation over communication networks whose channels are non-ideal, but noisy and/or intermittent. Channel intermittency captures randomness of network interconnections and packet-drop links. Based on input-output properties of feedback systems, we propose novel iterative algorithms that incorporate a networked feedback compensator to mitigate effects of the unreliable communication on distributed averaging. The new algorithms are time-invariant and do not suffer from the random walk behavior to additive noise of other average consensus algorithms. Moreover, the use of the link state information at the receiver leads to a new algorithm, which computes averages approximately correctly in the presence of intermittent communication and additive noise, under certain conditions.

Original languageEnglish (US)
Article number6422410
Pages (from-to)2231-2242
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume61
Issue number9
DOIs
StatePublished - Apr 22 2013
Externally publishedYes

Keywords

  • Additive noise
  • distributed averaging
  • link failures
  • mean square stability
  • random networks

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