On the Steady-State Analysis of PNLMS-Type Algorithms for Correlated Gaussian Input Data

Author

Kuhn, Eduardo Vinicius ; Das Chagas De Souza, Francisco ; Seara, Rui ; Morgan, D.R.

Author_Institution

Dept. of Electr. Eng., Fed. Univ. of Santa Catarina, Florianópolis, Brazil

Volume

21

Issue

11

fYear

2014

fDate

Nov. 2014

Firstpage

1433

Lastpage

1437

Abstract

This letter presents model expressions describing the steady-state behavior of proportionate normalized least-mean-square (PNLMS)-type algorithms, taking into account both complex- and real-valued correlated Gaussian input data. Specifically, based on energy-conservation arguments, general expressions for the excess mean-square error (EMSE) in steady state and misadjustment are obtained. Such general expressions are then applied to two well-known PNLMS-type algorithms, namely the improved PNLMS (IPNLMS) and the individual-activation-factor PNLMS (IAF-PNLMS). Simulation results are shown confirming the accuracy of the proposed model expressions under different operating conditions.

Keywords

Gaussian processes; adaptive filters; correlation theory; least mean squares methods; EMSE; IAF-PNLMS; IPNLMS; adaptive filters; correlated Gaussian input data; energy conservation arguments; excess mean square error; improved PNLMS algorithm; individual activation factor PNLMS; proportionate normalized least mean square; steady-state analysis; Accuracy; Algorithm design and analysis; Data models; Prediction algorithms; Signal processing algorithms; Signal to noise ratio; Steady-state; Adaptive filtering; excess mean-square error; misadjustment; proportionate normalized least-mean-square algorithm; steady-state behavior; stochastic model;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2014.2332751

Filename

6844847