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Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems

Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems
Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems
A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization of the channel and data estimation is decomposed into an iterative optimization loop. An efficient global optimization algorithm termed as the repeated weighted boosting aided search is employed first to identify the unknown SIMO channel model, and then the Viterbi algorithm is used for the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used for demonstrating the efficiency of this joint ML optimization scheme designed for blind adaptive SIMO systems.
201-205
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Yang, X.C.
24021645-9e0a-4495-b35c-817f6d550cef
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Yang, X.C.
24021645-9e0a-4495-b35c-817f6d550cef
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, S., Yang, X.C. and Hanzo, L. (2005) Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems. IEE 3G and Beyond, Savoy Place, London, United Kingdom. 07 - 09 Nov 2005. pp. 201-205 .

Record type: Conference or Workshop Item (Paper)

Abstract

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization of the channel and data estimation is decomposed into an iterative optimization loop. An efficient global optimization algorithm termed as the repeated weighted boosting aided search is employed first to identify the unknown SIMO channel model, and then the Viterbi algorithm is used for the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used for demonstrating the efficiency of this joint ML optimization scheme designed for blind adaptive SIMO systems.

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More information

Published date: 2005
Venue - Dates: IEE 3G and Beyond, Savoy Place, London, United Kingdom, 2005-11-07 - 2005-11-09
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 261682
URI: http://eprints.soton.ac.uk/id/eprint/261682
PURE UUID: fa4ef108-7642-41b5-9a4a-bfecfc70a71c
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 15 Dec 2005
Last modified: 18 Mar 2024 02:33

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Contributors

Author: S. Chen
Author: X.C. Yang
Author: L. Hanzo ORCID iD

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