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

Luo, Y and Chambers, JA (2002) Fast convergence algorithms for joint blind equalization and source separation based upon the cross-correlation and constant modulus criterion

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Abstract

To solve the problem of joint blind equalization and source separation, two new quasi-Newton adaptive algorithms with rapid convergence property are proposed based upon 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 cross-correlation 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Luo, YUNSPECIFIEDUNSPECIFIED
Chambers, JAj.a.chambers@surrey.ac.ukUNSPECIFIED
Date : 11 July 2002
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:27
Last Modified : 17 May 2017 13:27
URI: http://epubs.surrey.ac.uk/id/eprint/839268

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