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

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 expressio...

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Bibliographic Details
Published in:IEEE signal processing letters 2014-11, Vol.21 (11), p.1433-1437
Main Authors: Kuhn, Eduardo Vinicius, das Chagas de Souza, Francisco, Seara, Rui, Morgan, Dennis R.
Format: Article
Language:English
Subjects:
Accuracy
Adaptive filtering
Algorithm design and analysis
Algorithms
Computer simulation
Correlation
Correlation analysis
Data models
excess mean-square error
Gaussian
Mathematical models
misadjustment
Prediction algorithms
proportionate normalized least-mean-square algorithm
Signal processing
Signal processing algorithms
Signal to noise ratio
Steady state
steady-state behavior
stochastic model
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Summary: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.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2014.2332751