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Blind Joint Maximum Likelihood Channel Estimation and Data Detection for SIMO Systems

Blind Joint Maximum Likelihood Channel Estimation and Data Detection for SIMO Systems
Blind Joint Maximum Likelihood Channel Estimation and Data Detection for SIMO Systems
A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of singleinput multiple-output (SIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.
1476-8186
47-51
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Yang, X.C.
24021645-9e0a-4495-b35c-817f6d550cef
Chen, L.
586c0d55-dc72-49c0-a4fa-31df7000ce18
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Yang, X.C.
24021645-9e0a-4495-b35c-817f6d550cef
Chen, L.
586c0d55-dc72-49c0-a4fa-31df7000ce18
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, S., Yang, X.C., Chen, L. and Hanzo, L. (2007) Blind Joint Maximum Likelihood Channel Estimation and Data Detection for SIMO Systems. International Journal of Automation and Computing, 4 (1), 47-51.

Record type: Article

Abstract

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of singleinput multiple-output (SIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.

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Published date: January 2007
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 263364
URI: https://eprints.soton.ac.uk/id/eprint/263364
ISSN: 1476-8186
PURE UUID: 1080e81f-82e8-461b-a356-2486549d3899
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 01 Feb 2007
Last modified: 06 Jun 2018 13:14

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