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Computationally-Efficient DNLMS-Based Adaptive Algorithms for Echo Cancellation Application

Raymond Lee, Esam Abdel-Raheem, and Mohammed A.S. Khalid
Research Centre for Integrated Microsystems, Department of Electrical and Computer Engineering, University of Windsor, Windsor Ontario, Canada

Abstract—This paper investigates the application of thedelayed normalized least mean square (DNLMS) algorithmto echo cancellation. In order to reduce the amount of computations,DNLMS is modified by using computationallyefficienttechniques including the M-Max algorithm, a Stopand-go (SAG) algorithm, and Power-of-two (POT) quantization.For the SAG algorithm, a new stopping criterionrelated to the regressor energy is presented. Cumulatively,these modifications lead to reductions in power and/or area.Simulation results and comparisons with the normalizedleast mean square (NLMS) algorithm are included to showthe advantages of the computationally-efficient algorithms.

Index Terms—adaptive filtering, echo cancellation, NLMS,DNLMS

Cite: Raymond Lee, Esam Abdel-Raheem, and Mohammed A.S. Khalid, "Computationally-Efficient DNLMS-Based Adaptive Algorithms for Echo Cancellation Application," Journal of Communications, vol. 1, no. 7, pp. 1-8, 2006.

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