Performance Comparison Of The Blind Multi Channel Frequency Domain Normalized Lms And Variable Step-Size Lms With Noise
Year: 2007
Type: conference-paper
Abstract: The paper provides a comparative performance analysis of the normalized multichannel frequency-domain least-mean-squares (MCFLMS) and variable step size MCFLMS (VSS-MCFLMS) algorithms used in blind channel identification. Both the algorithms eliminate the need of a priori estimation of the step size parameter for rapid convergence to the desired solution. We perform the convergence analysis of the normalized MCFLMS (NMCFLMS) and show that even for a moderate SNR, the algorithm fails to converge to the eigenvector corresponding to the minimum eigenvalue of the data correlation matrix and hence misconverge to a fictitious solution. On the other hand, we show that the VSS-MCFLMS algorithm converges, both in noise-free and noisy conditions, to the eigenvector corresponding to the minimum eigenvalue and therefore more noise robust as compared to the NMCFLMS. The enhanced noise robustness of the VSS-MCFLMS algorithm over the NMCFLMS algorithm was verified using computer simulation results for a wide range of SNRs.
DOI: 10.5281/zenodo.40247
Authors: Haque, Ariful, Hasan, Kamrul
Institution: Bangladesh University of Engineering and Technology
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1505616507