Space-Time Block Code Recognition Algorithm Based on Multi-Modality Features Fusion Network

ZHANG Yu-yuan; YAN Wen-jun; ZHANG Li-min

doi:10.12263/DZXB.20210326

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Space-Time Block Code Recognition Algorithm Based on Multi-Modality Features Fusion Network

PAPERS | 更新时间：2025-12-08

- Space-Time Block Code Recognition Algorithm Based on Multi-Modality Features Fusion Network
- ACTA ELECTRONICA SINICA Vol. 51, Issue 2, Pages: 489-498(2023)
- 作者机构：
  
  海军航空大学信息融合研究所，山东烟台 264001
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210326
  CLC： TN911.7;
- Received：09 March 2021，
  
  Revised：2021-04-12，
  
  Published：25 February 2023
- 稿件说明：
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张聿远,闫文君,张立民.基于多模态特征融合网络的空时分组码识别算法[J].电子学报,2023,51(02):489-498.

ZHANG Yu-yuan,YAN Wen-jun,ZHANG Li-min.Space-Time Block Code Recognition Algorithm Based on Multi-Modality Features Fusion Network[J].ACTA ELECTRONICA SINICA,2023,51(02):489-498.
张聿远,闫文君,张立民.基于多模态特征融合网络的空时分组码识别算法[J].电子学报,2023,51(02):489-498. DOI： 10.12263/DZXB.20210326.

ZHANG Yu-yuan,YAN Wen-jun,ZHANG Li-min.Space-Time Block Code Recognition Algorithm Based on Multi-Modality Features Fusion Network[J].ACTA ELECTRONICA SINICA,2023,51(02):489-498. DOI： 10.12263/DZXB.20210326.

摘要

针对现有算法在空时分组码（Space-Time Block Code，STBC）识别过程中存在的低信噪比下误判概率高、识别效率低等问题，本文提出了一种基于多模态特征融合网络（Multi-Modality Features Fusion Network，MMFFN）的空时分组码自动识别方法.首先，在合并卷积层将STBC时域样本映射为一维特征向量的基础上，采用多扩张率下的扩张卷积提取非连续时间窗的STBC码内特征，实现多时延特征自提取；然后，构建多时序特征自提取模块以提取码间时序特征，进一步扩展映射特征类型；最后，将多时延拼接层获取的最大时延特征作为深层融合特征，并增加了带跨越连接的残差层以提升融合特征利用率，实现空时分组码识别.仿真实验结果表明，本文算法在-9dB下对6类STBC信号的识别准确率达到了90%以上，较现有识别算法的性能获得了显著提升，对低信噪比有较强的适应性.本文提出的STBC多时延特征提取和融合方法，为结合传统算法设计深度学习网络结构提供了新思路，其思想同样可应用于其他通信信号识别领域.

Abstract

Aiming at the problems of the existing algorithms in the process of space-time block code (STBC) recognition

such as high misdiagnosis probability and low recognition efficiency under low signal to noise ratio (SNR)

this paper proposes an automatic space-time block code recognition method based on multi-modality feature fusion network (MMFFN). Firstly

on the basis of mapping STBC time-domain samples into one-dimensional feature vectors by merging convolution layers

the dilated convolution at multiple dilation rates is used to extract STBC code features from discontinuous time windows

and the self-extraction of multi-delay features is realized. Then

the multi-sequence feature self-extraction module is constructed to extract the inter-code sequence feature

and the mapping feature types are further extended. Finally

the maximum delay feature of the multi-delay mosaic layer is extracted as the deep fusion feature

and the residual layer with span connection is added to improve the utilization of fusion feature and realize space-time block code recognition. Simulation results show that the recognition accuracy of the proposed algorithm for 6 types of STBC signals reaches more than 90% under -9dB

which is significantly improved compared with the performance of existing recognition algorithms

and has a strong adaptability to low SNR. The STBC multi-delay feature extraction and fusion method proposed in this paper provides a new idea for the design of deep learning network structure by combining traditional algorithms

and the idea can also be applied to other communication signal recognition fields.

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references

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