基于多模态特征融合网络的空时分组码识别算法  

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

作　　者：张聿远 闫文君 张立民 ZHANG Yu-yuan;YAN Wen-jun;ZHANG Li-min(Department of Information Fusion,Naval Astronautical University,Yantai,Shandong 264001,China)

机构地区：[1]海军航空大学信息融合研究所,山东烟台264001

出　　处：《电子学报》2023年第2期489-498,共10页Acta Electronica Sinica

基　　金：国家自然科学基金重大研究计划(No.91538201);泰山学者工程专项经费(No.Ts201511020)。

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.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.

关 键 词：空时分组码 深度学习 扩张卷积 多时延特征 多时序特征 最大时延融合 

分 类 号：TN911.7[电子电信—通信与信息系统]