Blind Joint Maximum Likelihood Channel Estimation and Data Detection for SIMO Systems
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.
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.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||01 Feb 2007|
|Last Modified:||02 Mar 2012 12:59|
|Contributors:||Chen, S. (Author)
Yang, X.C. (Author)
Chen, L. (Author)
Hanzo, L. (Author)
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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