ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
Selective Coefficient Update of Gradient-Based Adaptive Algorithms
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Analytical development of the MMAXNLMS algorithm
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 04
Analysis of adaptive filters using normalized signed regressor LMSalgorithm
IEEE Transactions on Signal Processing
Complexity reduction of the NLMS algorithm via selectivecoefficient update
IEEE Transactions on Signal Processing
A Comment on “Partial-Update NLMS Algorithms With Data-Selective Updating”
IEEE Transactions on Signal Processing
Partial-update NLMS algorithms with data-selective updating
IEEE Transactions on Signal Processing
A variable step size LMS algorithm
IEEE Transactions on Signal Processing
A new class of gradient adaptive step-size LMS algorithms
IEEE Transactions on Signal Processing
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
Performance analysis of the deficient length LMS adaptive algorithm
IEEE Transactions on Signal Processing - Part I
A class of adaptive step-size control algorithms for adaptivefilters
IEEE Transactions on Signal Processing
A Variable Step-Size Affine Projection Algorithm Designed for Acoustic Echo Cancellation
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Digital Signal Processing
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Selective partial update of the adaptive filter coefficients has been a popular method for reducing the computational complexity of least mean-square (LMS)-type adaptive algorithms. These algorithms use a fixed step-size that forces a performance compromise between fast convergence speed and small steady state misadjustment. This paper proposes a variable step-size (VSS) selective partial update LMS algorithm, where the VSS is an approximation of an optimal derived one. The VSS equations are controlled by only one parameter, and do not require any a priori information about the statistics of the system environment. Mean-square performance analysis will be provided for independent and identically distributed (i.i.d.) input signals, and an expression for the algorithm steady state excess mean-square error (MSE) will be presented. Simulation experiments are conducted to compare the proposed algorithm with existing full-update VSS LMS algorithms, which indicate that the proposed algorithm performs as well as these algorithms while requiring less computational complexity.