Reduced-complexity proportionate nlms employing block-based selective coefficient updates

TitleReduced-complexity proportionate nlms employing block-based selective coefficient updates
Publication TypeConference Paper
Year of Publication2008
AuthorsGordy, J. D., T. Aboulnasr, and M. Bouchard
Conference NameAcoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Pagination233 -236
Date Publishedmar.
Keywordsacoustic signal processing, block-based selective coefficient update algorithm, computational complexity, echo cancellation, echo suppression, input signal vector, least mean squares methods, normalized least-mean-square, posteriori error minimization, proportional weighting, ranking methods, reduced-complexity proportionate NLMS

This paper proposes a selective coefficient update algorithm for reducing the complexity of the proportionate normalized least- mean-square (P-NLMS) class of algorithms. It is shown that an optimal subset of coefficients to update, namely those minimizing the a posteriori error, cannot be constructed efficiently. A sub- optimal block-based coefficient selection algorithm is presented that combines proportional weighting of the input signal vector with fast ranking methods. It is compared to existing sub-optimal algorithms with respect to complexity overhead and convergence rate. Simulations show that the proposed algorithm produces performance approaching that of the optimal subset while maintaining a low coefficient selection overhead.


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