Fast convergence algorithms for joint blind equalization and source separation based upon the cross-correlation and constant modulus criterion

Luo, Yuhui; Chambers, Jonathon

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Fast convergence algorithms for joint blind equalization and source separation based upon the cross-correlation and constant modulus criterion

conference contribution

posted on 2010-01-12, 16:23 authored by Yuhui Luo, Jonathon Chambers

To solve the problem of joint blind equalization and source separation, two new quasi-Newton adaptive algorithms with rapid convergence property are proposed, based on the cross-correlation and constant modulus (CC-CM) criterion, namely the block-Shanno cross-correlation and constant modulus algorithm (BS-CCCMA) and the fast quasi-Newton crosscorrelation and constant modulus algorithm (FQN-CCCMA). Simulations studies are used to show that the convergence properties of these algorithms are much improved upon those of the conventional LMS-CCCMA algorithm

History

School

Mechanical, Electrical and Manufacturing Engineering

Citation

LUO, Y. and CHAMBERS, J., 2002. Fast convergence algorithms for joint blind equalization and source separation based upon the cross-correlation and constant modulus criterion. IN: IEEE International Conference on Acoustics, Speech, and Signal Processing, 13-17 May, Volume 3, pp. III-3065 - III-3068

Publisher

Version

VoR (Version of Record)

Publication date

2002

Notes

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