Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (5): 266-278.doi: 10.11896/jsjkx.211000085

• Computer Network • Previous Articles     Next Articles

Automatic Modulation Recognition Based on Deep Learning

JIAO Xiang1, WEI Xiang-lin2, XUE Yu1, WANG Chao1, DUAN Qiang2   

  1. 1 School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China
    2 The 63rd Research Institute,National University of Defense Technology,Nanjing 210007,China
  • Received:2021-10-13 Revised:2022-02-24 Online:2022-05-15 Published:2022-05-06
  • About author:JIAO Xiang,born in 1997,postgra-duate.Her main research interests include deep learning and modulation re-cognition.
    WEI Xiang-lin,born in 1985,Ph.D,associate research fellow.His main research interests include edge computing,deep learning and wireless network security.

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Abstract: Automatic modulation recognition (AMR) is critical to realize efficient spectrum sensing,spectrum management and spectrum utilization in non-cooperative communication scenarios.It is also an important prerequisite for efficient signal proces-sing.Traditional AMR methods based on pattern recognition need to extract features manually,which faces many problems such as high design complexity,low recognition accuracy and weak generalization ability.Therefore,practitioners turn to deep learning (DL) methods,which are good at extracting hidden features from the data,and propose a number of AMR-oriented deep neural network (ADNN) architectures.Compared with traditional methods,ADNN has achieved higher recognition accuracy,higher generalization ability and wider application range.This paper provides a comprehensive survey of ADNN to help practitioners understand the current research status in this field,and analyzes the future directions after pinpointing several open issues.Firstly,typical deep learning methods involved in ADNN design are introduced.Secondly,a few traditional AMR methods are briefly described.Thirdly,typical ADNNs are introduced in detail.Finally,a series of experiments are conducted on an open dataset to compare typical proposals,and several key research directions in this field are put forward.

Key words: Automatic modulation recognition, Deep learning, Deep neural network, Security

CLC Number: 

  • TN971
[1]LIANG Y C,TAN J J,DUSIT N.Overview on Intelligent Wireless Communication Technology[J].Journal on Communications,2020,41(7):1-17.
[2]SU W,XU J L,ZHOU M.Real-time modulation classificationbased on maximum likelihood[J].IEEE Communications Letters,2008,12(11):801-803.
[3]DOBRE O A,ABDI A,BAR-NESS Y,et al.Survey of automatic modulation classification techniques:classical approaches and new trends[J].IET Communications,2007,1(2):137-156.
[4]ALBAWI S,MOHAMMED T A,AL-ZAWI S.Understandingof a convolutional neural network[C]//2017 International Conference on Engineering and Technology (ICET).IEEE,2017:1-6.
[5]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural computation,1997,9(8):1735-1780.
[6]SCARSELLI F,GORI M,TSOI A C,et al.The graph neural network model[J].IEEE Transactions on Neural Networks,2008,20(1):61-80.
[7]GOODFELLOW I,BENGIO Y,COURVILLE A.MachineLearning Basics[J].Deep Learning,2016,1(7):98-164.
[8]YOUNG T,HAZARIKA D,PORIA S,et al.Recent Trends in Deep Learning Based Natural Language Processing[J].IEEE Computational Intelligence Magazine,2018,13(3):55-75.
[9]MASITA K L,HASAN A N,SHONGWE T.Deep Learning in Object Detection:a Review[C]//2020 International Conference on Artificial Intelligence,Big Data,Computing and Data Communication Systems (icABCD).IEEE,2020:1-11.
[10]COVINGTON P,ADAMS J,SARGIN E.Deep Neural Net-works for YouTube Recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems.ACM,2016:191-198.
[11]ZHANG X H,CAO X W.On Military Assistant Decision Based on Deep Learning[J].Fire Control & Command Control,2020,45(3):1-6.
[12]LECUN Y,BENGIO Y,HINTON G.Deep Learning[J].Nature,2015,521(7553):436-444.
[13]VARA P,D’SOUZAKEVIN B,BHARGAVAVIJAY K.ADownscaled Faster-RCNN Framework for Signal Detection and Time-frequency Localization in Wideband RF Systems[J].IEEE Transactions on Wireless Communications,2020,19(7):4847-4862.
[14]HE Z W,HOU S,ZHANG W C,et al.Multi-feature fusion classification method for communication specific emitter identification[J].Journal on Communications,2021,42(2):103-112.
[15]CYBENKO G.Approximation by superpositions of a sigmoidal function[J].Mathematics of Control,Signals and Systems,1989,2(4):303-314.
[16]DAYAN P,ABBOTT L F.Theoretical neuroscience:computa-tional and mathematical modeling of neural systems[M].Cambridge:MIT Press,2001.
[17]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1-9.
[18]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[19]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[20]TAN M,LE Q V.EfficientNet:Rethinking Model Scaling for Convolutional Neural Networks[C]//2019 International Conference on Machine Learning.ICML,2019:6105-6114.
[21]LIU W B,WANG Z D,LIU X H,et al.A survey of deep neural network architectures and their applications[J].Neurocompu-ting,2017,234(234):11-26.
[22]WU Z,PAN S,CHEN F,et al.A comprehensive survey ongraph neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2021,32(1):4-24.
[23]CHEN Y,ZHAO X,JIA X.Spectral-spatial classification ofhyperspectral data based on deep belief network[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(6):2381-2392.
[24]FISCHER A,IGEL C.Training restricted Boltzmann machines:An introduction[J].Pattern Recognition,2014,47(1):25-39.
[25]NG A.Sparse autoencoder[J].CS294A Lecture notes,2011,72:1-19.
[26]MA Z Y,HAN F L,XIE Z D,et al.Modulation recognition technology of satellite communication signal system[J].Acta Aeronautica et Astronautica Sinica,2014,35(12):3403-3414.
[27]PANAGIOTOU P,ANASTASOPOULOS A,POLYDOROS A.Likelihood ratio tests for modulation classification[C]//21st Century Military Communications.Architectures and Technologies for Information Superiority (Cat.No.00CH37155)(MILCOM 2000).IEEE,2000:670-674.
[28]HUAN C Y,POLYDOROS A.Likelihood methods for MPSKmodulation classification[J].IEEE Trans. Commun.,1995,43(2):1493-1504.
[29]WEN W,MENDEL J M.Maximum-likelihood classification for digital amplitude-phase modulations[J].IEEE Trans. Commun.,2000,48(2):189-193.
[30]CHAVALI V G,DA SILVA C R C M.Maximum-LikelihoodClassification of Digital Amplitude-Phase Modulated Signals in Flat Fading Non-Gaussian Channels[J].IEEE Transactions on Communications,2011,59(8):2051-2056.
[31]POPOOLA J J,OLST R V.A Novel Modulation-Sensing Me-thod[J].IEEE Vehicular Technology Magazine,2011,6(3):60-69.
[32]PARK C S,JANG W,NAH S P,et al.Automatic Modulation Recognition using Support Vector Machine in Software Radio Applications[C]//The 9th International Conference on Advanced Communication Technology.IEEE,2007:9-12.
[33]ASLAM M W,ZHU Z,NANDI A K.Automatic ModulationClassification Using Combination of Genetic Programming and KNN[J].IEEE Transactions on Wireless Communications,2012,11(8):2742-2750.
[34]PARK C S,CHOI J H,NAH S P.Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM[C]//2008 10th International Conference on Advanced Communication Technology.IEEE,2008:387-390.
[35]DAS D,ANAND A,BORA P K,et al.Cumulant based automatic modulation classification of QPSK,OQPSK,π/4-QPSK and 8-PSK in MIMO environment[C]//2016 International Conference on Signal Processing and Communications (SPCOM).IEEE,2016:1-5.
[36]XU Y,GE L,BO W.Digital Modulation Recognition MethodBased on Self-Organizing Map Neural Networks[C]//2008 4th International Conference on Wireless Communications,Networking and Mobile Computing.IEEE,2008:1-4.
[37]ORLIC V D,DUKIC M L.Multipath channel estimation algorithm for automatic modulation classification using sixth-order cumulants[J].Electronics Letters,2010,46(19):1348-1349.
[38]RF Datasets For Machine Learning[OL].https://www.deepsig.ai/datasets.
[39]TEKBIYIK K,EKTI A R,GÖRÇIN A,et al.Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels[C]//2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).IEEE,2020:1-6.
[40]O’SHEA T J,ROY T,CLANCY T C.Over the Air DeepLearning Based Radio Signal Classification[J].IEEE Journal of Selected Topics in Signal Processing,2018,12(1):168-179.
[41]WANG Y,LIU M,YANG J,et al.Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios[J].IEEE Transactions on Vehicular Technology,2019,68(4):4074-4077.
[42]HUANG S,JIANG Y,GAO Y,et al.Automatic ModulationClassification Using Contrastive Fully Convolutional Network[J].IEEE Wireless Communication Letters,2019,8(4):1044-1047.
[43]XU M,HOU J,WU P J,et al.Convolutional Neural Networks Based on Time-Frequency Characteristics for Modulation Classification[J].Computer Science,2020,47(2):175-179.
[44]ZENG Y,ZHANG M,HAN F,et al.Spectrum Analysis andConvolutional Neural Network for Automatic Modulation Recognition[J].Wireless Communications Letters IEEE,2019,8(3):929-932.
[45]ZHA X,PENG H,QIN X,et al.Modulation Recognition Me-thod Based on Multi-inputs Convolution Neural Network[J].Journal on Communications,2019,40(11):30-37.
[46]YI Y Q,LV L Q,LU Y Y,et al.Complex Signal Type Modulation Recognition Technology Based on Neural Network[J].Electronic Information Warfare Technology,2020,35(6):16-21.
[47]HUYNH-THE T,HUA C H,PHAM Q V,et al.MCNet:An Efficient CNN Architecture for Robust Automatic Modulation Classification[J].IEEE Communications Letters,2020,24(4):811-815.
[48]TUNZE G B,HUYNH-THE T,LEE J M,et al.Multi-shuffledConvolutional Blocks for Low-complex Modulation Recognition[C]//2020 International Conference on Information and Communication Technology Convergence (ICTC).IEEE,2020:939-942.
[49]TUNZE G B,HUYNH-THE T,LEE J M,et al.Sparsely Connected CNN for Efficient Automatic Modulation Recognition[J].IEEE Transactions on Vehicular Technology,2020,69(12):15557-15568.
[50]HUYNH-THE T,HUA C H,DOAN V S,et al.AccurateModulation Classification with Reusable-Feature Convolutional Neural Network[C]//2020 IEEE Eighth International Conference on Communications and Electronics (ICCE).IEEE,2021:12-17.
[51]HUYNH-THE T,DOAN V S,HUA C H,et al.Chain-Net:Learning Deep Model for Modulation Classification Under Synthetic Channel Impairment[C]//2020 IEEE Global Communications Conference(GLOBECOM 2020).IEEE,2020:1-6.
[52]LIN Y,TU Y,DOU Z.An Improved Neural Network Pruning Technology for Automatic Modulation Classification in Edge Devices[J].IEEE Transactions on Vehicular Technology,2020,69(5):5703-5706.
[53]WANG Y,YANG J,LIU M,et al.LightAMC:Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing[J].IEEE Transactions on Vehicular Techno-logy,2020,69(3):3491-3495.
[54]LIN Y,TU Y,DOU Z,et al.Contour Stella Image and DeepLearning for Signal Recognition in the Physical Layer[J].IEEE Transactions on Cognitive Communications and Networking,2021,7(1):34-46.
[55]IOFFE S,SZEGEDY C.Batch normalization:accelerating deepnetwork training by reducing internal covariate shift[C]//International Conference on International Conference on Machine Learning.JMLR,2015:448-456.
[56]WU H,LI Y,ZHOU L,et al.Convolutional neural network and multi-feature fusion for automatic modulation classification[J].Electronics Letters,2019,55(16):895-897.
[57]WANG Y,LIU M,YANG J,et al.Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios[J].IEEE Transactions on Vehicular Technology,2019,68(4):4074-4077.
[58]GREFF K,SRIVASTAVA R K,KOUTNíK J,et al.LSTM:A Search Space Odyssey[J].IEEE Transactions on Neural Networks & Learning Systems,2016,28(10):2222-2232.
[59]PENG C X,CHENG W,LI X B,et al.Method of Modulation Recognition for Communication Signals Based on STFT-BiLSTM[J].Journal of Air Force Early Warning Academy,2020,34(1):39-45.
[60]ZHA X,PENG H,QIN X,et al.Satellite Amplitude-Phase Signals Modulation Identification and Demodulation Algorithm Based on the Cyclic Neural Network[J].Acta Electronica Sinica,2019,47(11):2443-2448.
[61]YANG J.Research on Modulation Recognition for Communication Signals Based on Deep Learning[D].Changsha:National University of Defense Technology,2018.
[62]ZHANG B,LIU K,ZHAO M W.Deep Learning ModulationRecognition Algorithm Based on Time-frequency Analysis[J].Industrial Control Computer,2020,33(5):66-68,71.
[63]ZHANG Z,LUO H,WANG C,et al.Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure[J].IEEE Transactions on Vehicular Technology,2020,69(11):13521-13531.
[64]NJOKU J N,MOROCHO-CAYAMCELA M E,LIM W.CGDNet:Efficient Hybrid Deep Learning Model for Robust Automatic Modulation Recognition[J].IEEE Networking Letters,2021,3(2):47-51.
[65]LIU Y,LIU Y,YANG C.Modulation Recognition With GraphConvolutional Network[J].IEEE Wireless Communication Letters,2020,9(5):624-627.
[66]Automatic modulation recognition based on deep learning[OL].https://github.com/wzjialang/Automatic-modulation-recognition-based-on-deep-learning.
[67]O’SHEA T J,CORGAN J,CLANCY T C.Convolutional Radio Modulation Recognition Networks[C]//Engineering Applications of Neural Networks(EANN 2016).Springer,2016:213-226.
[68]CHEN J Y,CHENG K H,ZHENG H B.Deep Learning Based Modulation Recognition Method in Low SNR[J].Computer Science,2020,47(S1):283-288.
[69]LI R,LI L,YANG S,et al.Robust Automated VHF Modulation Recognition Based on Deep Convolutional Neural Networks[J].IEEE Communications Letters,2018,22(5):946-949.
[70]WEI S,KOSINSKI J A,MING Y.Dual-use of Modulation Reco-gnition Techniques for Digital Communication Signals[C]//2006 IEEE Long Island Systems,Applications and Technology Conference.IEEE,2006:1-6.
[71]XUE Y H.Automation modulation recognition of the communication signals based on deep learning[D].Harbin:Harbin Institute of Technology,2020.
[72]MA X B,ZHANG B N,GUO D X,et al.Modulation Recognition of Digital Communication Signals under Small Sample Conditions [J].Communications Technology,2020,53(11):2641-2646.
[73]LIN Y,ZHAO H,TU Y,et al.Threats of Adversarial Attacks in DNN-Based Modulation Recognition[C]//IEEE Conference on Computer Communications.IEEE,2006:2469-2478.
[74]WANG C,WEI X L,TIAN Q,et al.Feature Gradient-based Adversarial Attack on Modulation Recognition-oriented Deep Neural Networks[J].Computer Science,2021,48(7):25-32.
[75]LIN Y,ZHAO H,MA X,et al.Adversarial Attacks in Modulation Recognition With Convolutional Neural Networks[J].IEEE Transactions on Reliability,2021,70(1):389-401.
[76]YANG Y,CHEN M,WANG X Y,et al.Modulation Recognition based on Incremental Deep Learning[C]//2020 5th Interna-tional Conference on Mechanical,Control and Computer Engineering (ICMCCE).IEEE,2020:1701-1705.
[77]ZHANG S C,LIN Y,TU Y,et al.Electromagnetic signal modulation recognition technology based on lightweight deep neural network[J].Journal on Communications,2020,41(11):12-21.
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