Blind joint maximum likelihood channel estimation and data detection for SIMO systems

被引：6

作者：

Chen S. ^{[1
]}

Yang X.-C. ^{[1
]}

Chen L. ^{[1
]}

Hanzo L. ^{[1
]}

机构：

[1] School of Electronics and Computer Science, University of Southampton

来源：

International Journal of Automation and Computing | 2007年 / 4卷 / 1期

关键词：

Blind space-time equalisation; Maximum likelihood (ML) estimation; Single-input multiple-output (SIMO) systems;

D O I：

10.1007/s11633-007-0047-y

中图分类号：

学科分类号：

摘要：

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. © Institute of Automation, Chinese Academy of Sciences 2007.

引用

页码：47 / 51

页数：4

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