典型文献
Blind identification of space-time block codes based on deep learning
文献摘要：
Deep Learning(DL)has important applications to both commercial and military com-munications,such as software-defined radio,cognitive radio and spectrum surveillance.While DL has been intensively studied for modulation recognition,there are very few investigations for blind identification of Space-Time Block Codes(STBCs).This paper proposes a Residual Network(RN)-based model for identifying 6 kinds of STBC signals with a single receiving antenna,including the same length of coding matrix.In our work,we use the frequency-domain correlation function of a single time delay as the training data of DL model.Then,we explore the suitable RN structure for blind identification of STBCs.Finally,we compare the RN model with convolutional neural net-work and traditional method,and test the performance of RN model.Simulation results show that our RN-based model provides good performance with low sensitivity to decay of the dataset,such as sample length and data size.At the same time,better identification accuracy can be achieved under the condition of different modulation types and channel fading parameters at low Signal to Noise Ratio(SNR).
文献关键词：
中图分类号：
作者姓名：
Limin ZHANG;Yuyuan ZHANG;Wenjun YAN;Ling MA
作者机构：
Institute of Information Fusion,Naval Aeronautical University,Yantai 264001,China;Coastal Defense College,Naval Aeronautical University,Yantai 264001,China
文献出处：
引用格式：
[1]Limin ZHANG;Yuyuan ZHANG;Wenjun YAN;Ling MA-.Blind identification of space-time block codes based on deep learning)[J].中国航空学报（英文版）,2022(01):426-435
A类：
STBCs
B类：
Blind,identification,space,block,codes,deep,learning,Deep,Learning,DL,has,important,applications,both,commercial,military,munications,such,software,defined,radio,cognitive,spectrum,surveillance,While,been,intensively,studied,modulation,recognition,there,very,few,investigations,blind,Space,Time,Block,Codes,This,paper,proposes,Residual,Network,RN,model,identifying,kinds,signals,single,receiving,antenna,including,same,length,coding,matrix,In,our,we,use,frequency,domain,correlation,function,delay,training,Then,explore,suitable,structure,Finally,compare,convolutional,neural,net,traditional,method,test,performance,Simulation,results,show,that,provides,good,low,sensitivity,decay,dataset,sample,size,At,better,accuracy,can,achieved,under,condition,different,types,channel,fading,parameters,Signal,Noise,Ratio,SNR
AB值：
0.643339
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