Bias-compensated LMS algorithm for sparse systems over adaptive network

Wenxuan Han, Lijuan Jia*, Shunshoku Kanae, Zijiang Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

We propose bias-compensated algorithms based on the RZA-LMS algorithm and diffusion RZA-LMS algorithm. Our proposed algorithms improve the accuracy of estimation under the situation that input of the adaptive filter contains noise. Estimation methods of the input noise' variance are given for implementing our single-node and diffusion bias-compensated algorithms. Simulation results show that the proposed algorithms have better accuracy than algorithms without bias-compensation and the estimation results are unbiased under different noise levels.

Original languageEnglish
Title of host publicationProceedings of the 36th Chinese Control Conference, CCC 2017
EditorsTao Liu, Qianchuan Zhao
PublisherIEEE Computer Society
Pages8912-8916
Number of pages5
ISBN (Electronic)9789881563934
DOIs
Publication statusPublished - 7 Sept 2017
Event36th Chinese Control Conference, CCC 2017 - Dalian, China
Duration: 26 Jul 201728 Jul 2017

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference36th Chinese Control Conference, CCC 2017
Country/TerritoryChina
CityDalian
Period26/07/1728/07/17

Keywords

  • Bias-compensation
  • adaptive networks
  • diffusion networks
  • least mean squares
  • sparse system

Fingerprint

Dive into the research topics of 'Bias-compensated LMS algorithm for sparse systems over adaptive network'. Together they form a unique fingerprint.

Cite this