Convergence evaluation of a random step-size NLMS adaptive algorithm in system identification

S. A. Jimaa, T. Shimamura

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

Abstract

A new and simple method to adjust the step-size (μ) of the standard Normalized Least Mean Square (NLMS) adaptive algorithm is proposed here. The value of μ is totally controlled by the use of a Pseudorandom Noise (PRN) uniform distribution that is defined by values from 0 to 1. Randomizing the step-size parameter eliminates much of the trade-off between residual error and convergence speed compared with the fixed step-size. The mean-square error (MSE) of using the new algorithm in the adaptation process of system identification over a defined communication channel is investigated here. The proposed uniformly distributed step-size variation in the adaptation process of the NLMS algorithm makes it possible to have similar convergence rate but lower steady state error compared with fixed μ.

Original languageBritish English
Title of host publicationICSP2010 - 2010 IEEE 10th International Conference on Signal Processing, Proceedings
Pages135-138
Number of pages4
DOIs
StatePublished - 2010
Event2010 IEEE 10th International Conference on Signal Processing, ICSP2010 - Beijing, China
Duration: 24 Oct 201028 Oct 2010

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP

Conference

Conference2010 IEEE 10th International Conference on Signal Processing, ICSP2010
Country/TerritoryChina
CityBeijing
Period24/10/1028/10/10

Keywords

  • Adaptive algorithms
  • NLMS
  • System identification

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