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Pattern Recognition of Modulation signal Classification Using Deep Neural Networks

Computer systems science & Engineering 2022年第11期43卷 545-558页

作者： D.Venugopal V.Mohan s.Ramesh s.janupriya sangsoon Lim seifedine Kadry Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Saranathan College of EngineeringTrichy620012India Department of Computer science and Engineering Krishnasamy College of Engineering&TechnologyCuddalore607109India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Engineering Sungkyul UniversityAnyangKorea Department of Applied Data science Noroff University CollegeKristiansandNorway

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,*** becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex *** works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so *** addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal *** this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CsM-FFDNN).The goal of the CsM-FFDNN model is to classify the different types of digitally modulated *** proposed CsM-FFDNN model involves two major processes namely FE and *** proposed model uses sevcik Fractal Dimension(sFD)technique to extract the fractal features from the digital modulated ***,the extracted features are fed into the DNN model for modulation signal *** improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CsM-FFDNN *** experimental outcomes pointed out the superior recognition rate of the CsM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

关键词： Pattern recognition signal modulation communication signals deep learning feature extraction

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