Blind joint maximum likelihood channel estimation and data detection for SIMO systems |
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Authors: | Sheng Chen Xiao-Chen Yang Lei Chen Lajos Hanzo |
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Affiliation: | (1) School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK |
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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 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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Keywords: | Blind space-time equalisation single-input multiple-output(SIMO)systems maximum likelihood(ML)estimation |
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