On the Steady-State Analysis of PNLMS-Type Algorithms for Correlated Gaussian Input Data
Signal Processing Letters, IEEE （2014）
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.
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Key words
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,Adaptive filtering,excess mean-square error,misadjustment,proportionate normalized least-mean-square algorithm,steady-state behavior,stochastic model
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