The University of Southampton
University of Southampton Institutional Repository

Blind channel identification based on higher-order cumulant fitting using genetic algorithms

Blind channel identification based on higher-order cumulant fitting using genetic algorithms
Blind channel identification based on higher-order cumulant fitting using genetic algorithms
A family of blind equalisation algorithms identifies a channel model based on a higher-order cumulant (HOC) fitting approach. Since HOC cost functions are multimodal, gradient search techniques require a good initial estimate to avoid converging to local minima. We present a blind identification scheme which uses genetic algorithms (GAS) to optimise a HOC cost function. Because GAS are efficient global optimal search strategies, the proposed method guarantees to find a global optimal channel estimate. A micro- GA implementation is adopted to further enhance computational efficiency. As is demonstrated in computer simulation, this GA based scheme is robust and accurate, and has a fast convergence performance.
184-188
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Wu, Y.
84854e37-ada6-4cc8-995f-6ce5ebc77423
McLaughlin, S.
d8651585-025f-4ea9-bd15-cef87f323624
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Wu, Y.
84854e37-ada6-4cc8-995f-6ce5ebc77423
McLaughlin, S.
d8651585-025f-4ea9-bd15-cef87f323624

Chen, S., Wu, Y. and McLaughlin, S. (1997) Blind channel identification based on higher-order cumulant fitting using genetic algorithms. Proceedings of 5th IEEE Signal Processing Workshop on Higher-Order Statistics. pp. 184-188 .

Record type: Conference or Workshop Item (Other)

Abstract

A family of blind equalisation algorithms identifies a channel model based on a higher-order cumulant (HOC) fitting approach. Since HOC cost functions are multimodal, gradient search techniques require a good initial estimate to avoid converging to local minima. We present a blind identification scheme which uses genetic algorithms (GAS) to optimise a HOC cost function. Because GAS are efficient global optimal search strategies, the proposed method guarantees to find a global optimal channel estimate. A micro- GA implementation is adopted to further enhance computational efficiency. As is demonstrated in computer simulation, this GA based scheme is robust and accurate, and has a fast convergence performance.

Text
c-1997-spwhos - Author's Original
Download (427kB)

More information

Published date: 1997
Additional Information: 5th IEEE Signal Processing Workshop on Higher-Order Statistics (Banff, Alberta, Canada), July 21-23, 1997 Organisation: IEEE Signal Processing Society
Venue - Dates: Proceedings of 5th IEEE Signal Processing Workshop on Higher-Order Statistics, 1997-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 251017
URI: http://eprints.soton.ac.uk/id/eprint/251017
PURE UUID: 04ac2acc-0b1b-41db-9b7d-b861ac4cdbb8

Catalogue record

Date deposited: 31 Mar 2000
Last modified: 14 Mar 2024 05:07

Export record

Contributors

Author: S. Chen
Author: Y. Wu
Author: S. McLaughlin

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×