一种同型空时分组码的识别算法

doi:10.12263/DZXB.20210839

摘要/Abstract

摘要：

针对空时分组码(Space-Time Block Code，STBC)盲识别中码型相同的编码区分性较差甚至无法区分的问题，提出了一种基于接收信号统计特征的识别算法.首先分析了多输入多输出(Multiple Input Multiple Output，MIMO)系统中采用的空时编码方案与接收信号的统计特征之间的相关性，设计了概率匹配与弥散度匹配对该相关性进行量化，获得接收信号与不同编码方案的匹配度，最后利用决策树选择匹配度最高的编码作为识别结果.仿真结果表明，针对两组同型的空时分组码，所提算法在信噪比为8dB时可达98%以上的识别率，而基于特征提取的传统算法无法对两组编码进行有效区分；与基于深度学习的算法相比，本文算法对同型空时码的识别具有更好的鲁棒性，识别过程使用更少的计算资源.

关键词: 多输入多输出, 正交空时分组码, 准正交空时分组码, 决策树, 统计特征, 盲识别

Abstract:

To solve the problem that space-time block codes(STBCs) with the same shape are poor differentiation or even indistinguishable, a recognition algorithm based on probability matching and dispersion matching was proposed. Firstly, the correlation between the STBCs adopted by multiple input multiple output(MIMO) system and the statistical characteristics of the received signal is analyzed. Probability matching and dispersion matching are designed to quantify the correlation, and the matching degree between the received signal and different codes is obtained. Finally, the decision tree is used to select the code with the highest matching degree as the recognition result. Simulation results show that the proposed algorithm can achieve more than 98% recognition rate when SNR is 8dB, while the traditional algorithm based on feature extraction cannot effectively distinguish the in-group codes. Compared with the algorithm based on deep learning, the proposed algorithm has better robustness for the recognition of space-time block codes with the same shape, and has better real-time performance and better applicability with fewer computing resources.

Key words: multiple input multiple output, orthogonal space-time block code, quasi-orthogonal space-time block code, decision tree, statistical characteristic, blind recognition

中图分类号:

TN911.7

王玉龙, 吴迪, 胡涛. 一种同型空时分组码的识别算法[J]. 电子学报, 2022, 50(11): 2754-2764.

WANG Yu-long, WU Di, HU Tao. An Identification Algorithm for Space-Time Block Codes with the Same Shape[J]. Acta Electronica Sinica, 2022, 50(11): 2754-2764.

图/表 14

表1 不同延时下||RX,T(τ)||F2的统计结果

Code	$τ$	$\| \| R X, T (τ) \| \| F 2$
OSTBC₁（3，4）	0	$0$
	1	$0.25$
	2	$0.125$
	3	$0$
	>3	$0$
OSTBC₂（3，4）	0	$0$
	1	$0.125$
	2	$0.125$
	3	$0.125$
	>3	$0$
OSTBC₃（3，4）	0	$0$
	1	$0.1875$
	2	$0.125$
	3	$0.0625$
	>3	$0$

表1 不同延时下||RX,T(τ)||F2的统计结果

Code	$τ$	$\| \| R X, T (τ) \| \| F 2$
OSTBC₁（3，4）	0	$0$
	1	$0.25$
	2	$0.125$
	3	$0$
	>3	$0$
OSTBC₂（3，4）	0	$0$
	1	$0.125$
	2	$0.125$
	3	$0.125$
	>3	$0$
OSTBC₃（3，4）	0	$0$
	1	$0.1875$
	2	$0.125$
	3	$0.0625$
	>3	$0$

图1 三种同型空时编码的四阶累积量随信噪比的变化趋势

表2 空时码组ω1的参数对比

Code	$G 1, Λ R \| G 1, Λ I$	$G 2, Λ R \| G 2, Λ I$	$G 3, Λ R \| G 3, Λ I$	$G 4, Λ R \| G 4, Λ I$
$O S T B C 1 (3,4)$	${1,1} {1,1}$	${1,1} {1,1}$	${1,1, 1} {1,1, 1}$	${1,1} {1,1}$
$O S T B C 2 (3,4)$	${1,1, 1} {1,1, 1}$	${1,1} {1,1}$	${1,1} {1,1}$	${1,1} {1,1}$
$O S T B C 3 (3,4)$	${1,1, 12} {1,1, 12}$	${1,1, 12} {1,1, 12}$	${1, 2} {1, 2}$	${1} {1}$

表2 空时码组ω1的参数对比

Code	$G 1, Λ R \| G 1, Λ I$	$G 2, Λ R \| G 2, Λ I$	$G 3, Λ R \| G 3, Λ I$	$G 4, Λ R \| G 4, Λ I$
$O S T B C 1 (3,4)$	${1,1} {1,1}$	${1,1} {1,1}$	${1,1, 1} {1,1, 1}$	${1,1} {1,1}$
$O S T B C 2 (3,4)$	${1,1, 1} {1,1, 1}$	${1,1} {1,1}$	${1,1} {1,1}$	${1,1} {1,1}$
$O S T B C 3 (3,4)$	${1,1, 12} {1,1, 12}$	${1,1, 12} {1,1, 12}$	${1, 2} {1, 2}$	${1} {1}$

表3 空时码组ω2的参数对比

Code	$G 1, Λ R G 1, Λ I$	$G 2, Λ R \| G 2, Λ I$	$G 3, Λ R \| G 3, Λ I$	$G 4, Λ R \| G 4, Λ I$
$O S T B C 1 (4,4)$	${1,1, 1} {1,1, 1}$	${1,1, 1} {1,1, 1}$	${1,1, 1} {1,1, 1}$	${1,1, 1} {1,1, 1}$
$O S T B C 2 (4,4)$	${1,1, 2} {1,1, 2}$	${1,1} {1,1}$	${1,1, 2} {1,1, 2}$	${1} {1,2}$
$Q O S T B C 3 (4,4)$	${1,1, 1,1} {1,1, 1,1}$	${1,1, 1,1} {1,1, 1,1}$	${1,1, 1,1} {1,1, 1,1}$	${1,1, 1,1} {1,1, 1,1}$

表3 空时码组ω2的参数对比

Code	$G 1, Λ R G 1, Λ I$	$G 2, Λ R \| G 2, Λ I$	$G 3, Λ R \| G 3, Λ I$	$G 4, Λ R \| G 4, Λ I$
$O S T B C 1 (4,4)$	${1,1, 1} {1,1, 1}$	${1,1, 1} {1,1, 1}$	${1,1, 1} {1,1, 1}$	${1,1, 1} {1,1, 1}$
$O S T B C 2 (4,4)$	${1,1, 2} {1,1, 2}$	${1,1} {1,1}$	${1,1, 2} {1,1, 2}$	${1} {1,2}$
$Q O S T B C 3 (4,4)$	${1,1, 1,1} {1,1, 1,1}$	${1,1, 1,1} {1,1, 1,1}$	${1,1, 1,1} {1,1, 1,1}$	${1,1, 1,1} {1,1, 1,1}$

图2 弥散度的匹配与失配

图3 不同误差距离下弥散度随信噪比的变化趋势

图4 空时码组ω1的识别方案

图5 空时码组ω2的识别方案

图6 不同识别方案下的识别率对比

表4 不同算法的计算量对比

算法对比	加法次数	乘法次数
本文算法	15N	5N
文献［18］	144816N	145408N
文献［19］	74336N	74752N

图7 不同信道参数及接收天线数目下算法识别率对比

图8 不同调制方式下的识别率对比

图9 不同接收样本数下的识别率对比

图10 不同弥散度阈值下的识别率对比

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