Distributed average consensus with bounded quantization

Shengyu Zhu, Biao Chen

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

2 Scopus citations

Abstract

This paper considers distributed average consensus using bounded quantizers with potentially unbounded input data. We develop a quantized consensus algorithm based on a distributed alternating direction methods of multipliers (ADMM) algorithm. It is shown that, within finite iterations, all the agent variables either converge to the same quantization point or cycle with a finite period. In the convergent case, we derive a consensus error bound which also applies to that of the unbounded rounding quantizer provided that the desired average lies within quantizer output range. Simulations show that the proposed algorithm almost always converge when the network becomes large and dense.

Original languageEnglish (US)
Title of host publicationSPAWC 2016 - 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509017492
DOIs
StatePublished - Aug 9 2016
Event17th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2016 - Edinburgh, United Kingdom
Duration: Jul 3 2016Jul 6 2016

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2016-August

Other

Other17th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2016
Country/TerritoryUnited Kingdom
CityEdinburgh
Period7/3/167/6/16

Keywords

  • Quantized consensus
  • alternating direction method of multipliers (ADMM)
  • bounded quantization
  • distributed averaging algorithm
  • finite-level quantizer

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

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