基于独立分量分析的实正交空时分组码盲识别

doi:10.3969/j.issn.1000-436x.2012.11.001

Abstract

Abstract:

A method for blind recognition of orthogonal space-time block coding (OSTBC) based on independent component analysis (ICA) was proposed.Firstly,the model of received signal was given.And then independent component analysis technology was used to separate the virtual channel matrix which contains coding matrix information.Using the characteristics of coding matrix,it was proved that the correlation matrix of the virtual channel matrix in OSTBC communication systems was diagonal matrix.Finally,two characteristic parameters were proposed: sparsity and variance,which could recognize OSTBC.Simulation results demonstrated that the proposed method is able to identify OSTBC while achieves good performance.

Key words: MIMO systems, orthogonal space-time block coding, blind recognition, virtual channel matrix

Zhi-jin ZHAO,Lin CHEN,Hai-quan WANG,Lei SHEN. Blind recognition of real orthogonal STBC based on independent component analysis[J]. Journal on Communications, 2012, 33(11): 1-7.

Figures/Tables 10

n_R	正交空时分组码 $\hat{A}$	非正交空时分组码 $\hat{A}$
n_R=1	$[\begin{matrix} 30.5691 & - 0.5067 & 0.2263 & - 1.4978 \\ - 0.5067 & 29.9989 & - 0.3723 & 0.6550 \\ 0.2263 & - 0.3723 & 29.8941 & 0.7057 \\ - 1.4978 & 0.6550 & 0.7057 & 29.5379 \end{matrix}]$	$[\begin{matrix} 28.7895 & 1.3533 & - 7.3138 & - 3.1636 \\ 1.3533 & 31.0566 & - 2.0280 & - 1.3136 \\ - 7.3138 & - 2.0280 & 30.4094 & - 2.2663 \\ - 3.1636 & - 1.3136 & - 2.2663 & 29.5151 \end{matrix}]$
n_R=2	$[\begin{matrix} 30.8392 & 0.3310 & - 0.6614 & 0.7853 \\ 0.3310 & 30.4584 & 0.2703 & - 0.7554 \\ - 0.6614 & 0.2703 & 30.3270 & - 0.0888 \\ 0.7853 & - 0.7554 & - 0.0888 & 29.5754 \end{matrix}]$	$[\begin{matrix} 30.7348 & 1.9415 & - 1.4580 & - 0.8610 \\ 1.9415 & 30.1225 & - 1.7593 & 2.2053 \\ - 1.4580 & - 1.7593 & 29.2791 & 0.6580 \\ - 0.8610 & 2.2053 & 0.6580 & 29.8187 \end{matrix}]$

n_R	正交空时分组码 $\hat{A}$	非正交空时分组码 $\hat{A}$
n_R=1	$[\begin{matrix} 40.5572 & 0.3766 & - 0.0147 & - 0.4304 \\ 0.3766 & 39.9744 & 1.6558 & - 0.0477 \\ - 0.0147 & 1.6558 & 40.1707 & 0.6250 \\ - 0.4304 & - 0.0477 & 0.6250 & 39.4499 \end{matrix}]$	$[\begin{matrix} 53.3838 & - 2.9486 & - 6.8585 & 0.6519 \\ - 2.9486 & 41.8764 & 6.3582 & 1.3846 \\ - 6.8585 & 6.3582 & 39.5942 & 0.5691 \\ 0.6519 & 1.3846 & 0.5691 & 21.4551 \end{matrix}]$
n_R=2	$[\begin{matrix} 35.0483 & - 1.1796 & - 0.8304 & - 1.2195 \\ - 1.1796 & 34.9706 & 0.7818 & - 0.3830 \\ - 0.8304 & 0.7818 & 34.5182 & - 0.8047 \\ - 1.2195 & - 0.3830 & - 0.8047 & 34.9130 \end{matrix}]$	$[\begin{matrix} 36.7562 & - 1.2168 & 2.5448 & - 2.3932 \\ - 1.2168 & 35.7604 & - 4.5481 & 5.3259 \\ 2.5448 & - 4.5481 & 33.6259 & - 1.0237 \\ - 2.3932 & 5.3259 & - 1.0237 & 34.4099 \end{matrix}]$

References 10

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