Proportionate Normalized Least Mean Square Algorithms Based on Coefficient Difference

Ligang LIU  Masahiro FUKUMOTO  Sachio SAIKI  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E93-A   No.5   pp.972-975
Publication Date: 2010/05/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E93.A.972
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Digital Signal Processing
adaptive filtering algorithm,  proportionate adaptation,  system identification,  least-mean-squares,  sparse impulse response,  

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The proportionate normalized least mean square algorithm (PNLMS) greatly improves the convergence of the sparse impulse response. It exploits the shape of the impulse response to decide the proportionate step gain for each coefficient. This is not always suitable. Actually, the proportionate step gain should be determined according to the difference between the current estimate of the coefficient and its optimal value. Based on this idea, an approach is proposed to determine the proportionate step gain. The proposed approach can improve the convergence of proportionate adaptive algorithms after a fast initial period. It even behaves well for the non-sparse impulse response. Simulations verify the effectiveness of the proposed approach.