Distributed average consensus with bounded quantization

Shengyu Zhu; Biao Chen

doi:10.1109/SPAWC.2016.7536852

Distributed average consensus with bounded quantization

Shengyu Zhu, Biao Chen

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference 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 language	English (US)
Title of host publication	SPAWC 2016 - 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509017492
DOIs	https://doi.org/10.1109/SPAWC.2016.7536852
State	Published - Aug 9 2016
Event	17th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2016 - Edinburgh, United Kingdom Duration: Jul 3 2016 → Jul 6 2016

Publication series

Name	IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume	2016-August

Other

Other	17th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2016
Country/Territory	United Kingdom
City	Edinburgh
Period	7/3/16 → 7/6/16

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

Access to Document

10.1109/SPAWC.2016.7536852

Cite this

Zhu, S., & Chen, B. (2016). Distributed average consensus with bounded quantization. In SPAWC 2016 - 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications Article 7536852 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2016-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPAWC.2016.7536852

Distributed average consensus with bounded quantization. / Zhu, Shengyu; Chen, Biao.
SPAWC 2016 - 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications. Institute of Electrical and Electronics Engineers Inc., 2016. 7536852 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2016-August).

Research output: Chapter in Book/Entry/Poem › Conference contribution

Zhu, S & Chen, B 2016, Distributed average consensus with bounded quantization. in SPAWC 2016 - 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications., 7536852, IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, vol. 2016-August, Institute of Electrical and Electronics Engineers Inc., 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2016, Edinburgh, United Kingdom, 7/3/16. https://doi.org/10.1109/SPAWC.2016.7536852

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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.",

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AB - 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.

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Distributed average consensus with bounded quantization

Abstract

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Keywords

ASJC Scopus subject areas

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