Complexity reduction of the NLMS algorithm via selective coefficient update

TitleComplexity reduction of the NLMS algorithm via selective coefficient update
Publication TypeJournal Article
Year of Publication1999
AuthorsAboulnasr, T., and K. Mayyas
JournalSignal Processing, IEEE Transactions on
Pagination1421 -1424
Date Publishedmay.
Keywordsadaptive echo cancellation, adaptive filters, adaptive signal processing, complexity reduction, computational complexity, filtering theory, finite impulse response, FIR filters, i.i.d. signals, independent identically distributed signals, least mean squares methods, MSE convergence, NLMS algorithm, NLMS FIR adaptive filter, normalized least mean square, one update/iteration, partial coefficient update, selective coefficient update, steady-state performance

This article proposes an algorithm for partial update of the coefficients of the normalized least mean square (NLMS) finite impulse response (FIR) adaptive filter. It is shown that while the proposed algorithm reduces the complexity of the adaptive filter, it maintains the closest performance to the full update NLMS filter for a given number of updates. Analysis of the MSE convergence and steady-state performance for independent and identically distributed (i.i.d.) signals is provided for the extreme case of one update/iteration


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