An Identification Algorithm for Space-Time Block Codes with the Same Shape

WANG Yu-long; WU Di; HU Tao

doi:10.12263/DZXB.20210839

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An Identification Algorithm for Space-Time Block Codes with the Same Shape

PAPERS | 更新时间：2025-12-08

- An Identification Algorithm for Space-Time Block Codes with the Same Shape
- ACTA ELECTRONICA SINICA Vol. 50, Issue 11, Pages: 2754-2764(2022)
- 作者机构：
  
  解放军战略支援部队信息工程大学, 河南郑州 450001
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210839
  CLC： TN911.7
- Received：02 July 2021，
  
  Revised：2021-11-04，
  
  Published：25 November 2022
- 稿件说明：
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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.
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. DOI： 10.12263/DZXB.20210839.

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. DOI： 10.12263/DZXB.20210839.

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.

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.

关键词

Keywords

references

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