FOLMS-AMDCNet:an automatic recognition scheme for multiple-antenna OFDM systems  

作　　者：ZHANG Yuyuan YAN Wenjun ZHANG Limin LING Qing 

机构地区：[1]Academy of Aeronautical Operations Service,Naval Aviation University,Yantai 264001,China

出　　处：《Journal of Systems Engineering and Electronics》2023年第2期307-323,共17页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(91538201);the Taishan Scholar Foundation of China(ts201511020).

摘　　要：The existing recognition algorithms of space-time block code(STBC)for multi-antenna(MA)orthogonal frequencydivision multiplexing(OFDM)systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment.However,owing to the restrictions on the prior information and channel conditions,these existing algorithms cannot perform well under strong interference and noncooperative communication conditions.To overcome these defects,this study introduces deep learning into the STBCOFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum(FOLMS)and attention-guided multi-scale dilated convolution network(AMDCNet).The fourth-order lag moment vectors of the received signals are calculated,and vectors are stitched to form two-dimensional FOLMS,which is used as the input of the deep learning-based model.Then,the multi-scale dilated convolution is used to extract the details of images at different scales,and a convolutional block attention module(CBAM)is introduced to construct the attention-guided multi-scale dilated convolution module(AMDCM)to make the network be more focused on the target area and obtian the multi-scale guided features.Finally,the concatenate fusion,residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types.Simulation experiments show that the average recognition probability of the proposed method at−12 dB is higher than 98%.Compared with the existing algorithms,the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances.In addition,the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise,which is more suitable for non-cooperative communication systems than the existing algorithms.

关 键 词：blind signal identification(BSI) space-time block code(STBC) orthogonal frequency-division multiplexing(OFDM) deep learning fourth-order lag moment spectrum(FOLMS) 

分 类 号：TN929.53[电子电信—通信与信息系统]