Distributed consensus with quantized data via sequence averaging

Authors:
Jun Fang;Hongbin Li
Affiliations:
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 12
Cited 0

Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Distributed average consensus with least-mean-square deviation

Journal of Parallel and Distributed Computing
Quantized consensus

Automatica (Journal of IFAC)
Polynomial filtering for fast convergence in distributed consensus

IEEE Transactions on Signal Processing
Accelerated distributed average consensus via localized node state prediction

IEEE Transactions on Signal Processing
Decentralized Maximum-Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems

IEEE Transactions on Signal Processing - Part I
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals

IEEE Transactions on Signal Processing
Distributed Average Consensus With Dithered Quantization

IEEE Transactions on Signal Processing - Part I
Coding With Side Information for Rate-Constrained Consensus

IEEE Transactions on Signal Processing - Part I
The capacity of wireless networks

IEEE Transactions on Information Theory
Consensus Propagation

IEEE Transactions on Information Theory
Distributed computation of averages over ad hoc networks

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	35.68

Visualization

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.