An Adaptive Combination Rule for Diffusion LMS Based on Consensus Propagation

Ayano Nakai; Kazunori Hayashi

ICASSP 2018accepted0 citations

An Adaptive Combination Rule for Diffusion LMS Based on Consensus Propagation

Abstract

Diffusion least-mean-square (LMS) algorithm is a method that estimates an unknown global vector from its linear measurements obtained at multiple nodes in a network in a distributed manner. This paper proposes a novel combination rule in the algorithm used to integrate the local estimates at each node by using the idea of consensus propagation, which is known to be a fast algorithm to achieve the average consensus. Moreover, we optimize constants involved in the proposed combination rule in terms of the steady state mean-square-deviation (MSD) and show an adaptive combination rule, along with an adaptive implementation. Simulation results demonstrate that the proposed combination scheme achieves better MSD performance than conventional combination schemes.

BibTeX

@inproceedings{icassp2018_anadaptivecombin,
  title = {An Adaptive Combination Rule for Diffusion LMS Based on Consensus Propagation},
  author = {Ayano Nakai and Kazunori Hayashi},
  booktitle = {ICASSP 2018},
  year = {2018}
}