Set-membership proportionate affine projection algorithms
EURASIP Journal on Audio, Speech, and Music Processing
Time-domain convolutive blind source separation employing selective-tap adaptive algorithms
EURASIP Journal on Audio, Speech, and Music Processing
Selective partial update and set-membership subband adaptive filters
Signal Processing
IEEE Transactions on Signal Processing
Journal of Signal Processing Systems
Limited feedback multiuser MIMO techniques for time-correlated channels
EURASIP Journal on Advances in Signal Processing - Multiuser MIMO Transmission with Limited Feedback, Cooperation, and Coordination
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in theory and methods for nonstationary signal analysis
A variable step-size selective partial update LMS algorithm
Digital Signal Processing
Modified quasi-OBE algorithm with improved numerical properties
Signal Processing
Digital Signal Processing
Hi-index | 35.69 |
In this paper, we present mean-squared convergence analysis for the partial-update normalized least-mean square (PU-NLMS) algorithm with closed-form expressions for the case of white input signals. The formulae presented here are more accurate than the ones found in the literature for the PU-NLMS algorithm. Thereafter, the ideas of the partial-update NLMS-type algorithms found in the literature are incorporated in the framework of set-membership filtering, from which data-selective NLMS-type algorithms with partial-update are derived. The new algorithms, referred to herein as the set-membership partial-update normalized least-mean square (SM-PU-NLMS) algorithms, combine the data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. A thorough discussion of the SM-PU-NLMS algorithms follows, whereby we propose different update strategies and provide stability analysis and closed-form formulae for excess mean-squared error (MSE). Simulation results verify the analysis for the PU-NLMS algorithm and the good performance of the SM-PU-NLMS algorithms in terms of convergence speed, final misadjustment, and computational complexity.