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

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conference contribution
posted on 12.01.2010 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

© IEEE

Version

VoR (Version of Record)

Publication date

2002

Notes

This is a conference paper [© IEEE]. It is also available from: http://ieeexplore.ieee.org/servlet/opac?punumber=7874. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

0780374029

ISSN

1520-6149

Language

en

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