Distributed consensus with quantized data via sequence averaging

Jun Fang, Hongbin Li

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

The problem of distributed average consensus with quantized data is considered in this correspondence. Conventional consensus algorithms suffer from divergence when quantization errors are present. To address this issue, we introduce a modified quantization-based consensus protocol and exploit the temporal information collected from the iterative process, based on which we develop an efficient consensus algorithm. The proposed consensus algorithm is proved to converge to the true mean, i.e., the average of the initial state, in a mean square sense. It also presents an advantage of speeding up the convergence over the algorithm [P. Frasca, R. Carli, F. Fagnani, and S. Zampieri, "Average Consensus on Networks With Quantized Communication," Int. J. Robust Non-Linear Control, 2008, to be published] without exploitation of temporal information. Numerical results are presented to illustrate the effectiveness of the proposed algorithm.

Original languageEnglish
Article number5256252
Pages (from-to)944-948
Number of pages5
JournalIEEE Transactions on Signal Processing
Volume58
Issue number2
DOIs
StatePublished - Feb 2010

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