Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 310-317.doi: 10.11896/JsJkx.190800073

• Computer Network • Previous Articles     Next Articles

Radio Modulation Recognition Based on Signal-noise Ratio Classification

CHEN Jin-yin, JIANG Tao and ZHENG Hai-bin   

  1. College of Information Engineering,ZheJiang University of Technology,Hangzhou 310000,China
  • Published:2020-07-07
  • About author:CHEN Jin-yin , Ph.D, associate professor.Her main research interests include artificial intelligence security, graph data mining and evolutionary computing.
  • Supported by:
    This work was supported by the ZheJiang Provincial Natural Science Foundation of China (LY19F020025),MaJor Special Funding for “Science and Technology Innovation 2025” in Ningbo (2018B10063),Signal Recognition Based on GAN,Deep Learning for Enhancement Recognition ProJect ,Engineering Research Center of Cognitive Healthcare of ZheJiang Province (2018KFJJ07).

Abstract: Radio modulation recognition has been widely used in various fields of military and civilian.Compared with the traditional methods such as artificial recognition and spectrum analysis,the modulation recognition method based on deep learning has better performance,but it still has the problem of low recognition accuracy.This paper proposed a modulation recognition method based on long-term and short-term memory network (LSTM) model.It combines deep learning classification method with SNR classification to design a SNR modulation recognition framework based on deep learning.By accurately classifying high and low SNR signals and using different denoising processing,the recognition accuracy of low SNR signal modulation is improved.The recognition accuracy of 2016.4c signal data set by machine learning method is 21%.Three modulation type identification comparison experiments,non-denoising,grading denoising and total denoising,are carried out on 2016.4C signal data set,the recognition accuracy is 69.82%,70.56%,and 66.67% respectively,which effectively verifies the feasibility and superiority of the proposed method to improve the accuracy of low SNR signal recognition.

Key words: Signal-noise ratio classification, Modulation recognition, Deep learning, Long-short term memory networks

CLC Number: 

  • TP29
BAO H,WANG Y,CHEN L.Digital Signal Modulation Recognition Equipment Based on High-order Cumulants//Proceedings of the 11th EAI International Conference on Mobile Multimedia Communications.ICST (Institute for Computer Sciences,Social-Informatics and Telecommunications Engineering),2018:290-297.
MINGQUAN L,XIANCI X,LEMING L.Cyclic spectral features based modulation recognition//Proceedings of International Conference on Communication Technology(ICCT’96).IEEE,1996,2:792-795.
CAI T,WANG C,CUI G,et al.Constellation-wavelet transform automatic modulation identifier for M-ary QAM signals//2015 IEEE 26th Annual International Symposium on Personal,Indoor,and Mobile Radio Communications (PIMRC).IEEE,2015:212-216.
YIN C,LI B,LI Y.Modulation classification of MQAM signals from their constellation using clustering//2010 Second International Conference on Communication Software and Networks.IEEE,2010:303-306.
JIA Y U,CHEN Y.Digital modulation recognition method based on BP neural network.Transducer Microsyst,Technol,2012,5(7).
PETROVA M,MHNEN P,OSUNA A.Multi-class classification of analog and digital signals in cognitive radios using support vector machines//2010 7th International Symposium on Wireless Communication Systems.IEEE,2010:986-990.
O’SHEA T J,CORGAN J,CLANCY T C.Convolutional radio modulation recognition networks//International Conference on Engineering Applications of Neural Networks.Springer,Cham,2016:213-226.
WEST N E,O’SHEA T.Deep architectures for modulation recognition//2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).IEEE,2017:1-6.
ZHANG D W,DONG J,LIU K,et al.Quality Evaluation of Auto-adaptative Frequency-hopping Channel Based on Receiving Signal-to-noise Ratio Prediction.Ship Electronic Engineering,2008(4):76-79.
CHANG L L,DAI X H,L Y,et al.Signal-to-Noise Ratio Prediction Method for High Speed Mobile Communication System.Journal of Circuits and Systems,2012,17(1):24-30.
CUI L F,CHENG Y C,YANG C,et al.Prediction of Signal-to-Noise Ratio Based on Genetic Optimized BP Neural Network.China New Communications,2016(7):77-77.
FEI C L,CHENG Y C,YANG C,et al.Application of Particle Swarm Optimization BP Neural Network in Signal-to-Noise Ratio Prediction.China New Communications,2016(6):44-44.
VINCENT P,LAROCHELLE H,LAJOIE I,et al.Stacked denoising autoencoders:Learning useful representations in a deep network with a local denoising criterion.Journal of Machine Learning Research,2010,11:3371-3408.
DESIMIO M P,PRESCOTT G E.Adaptive generation of decision functions for classification of digitally modulated signals//Proceedings of the IEEE 1988 National Aerospace and Electronics Conference.IEEE,1988:1010-1014.
GHANI N,LAMONTAGNE R.Neural networks applied to the classification of spectral features for automatic modulation recognition//Proceedings of MILCOM’93-IEEE Military Communications Conference.IEEE,1993:111-115.
LOPATKA J,PEDZISZ M.Automatic modulation classification using statistical moments and a fuzzy classifier//WCC 2000-ICSP 2000.2000 5th International Conference on Signal Processing Proceedings.16th World Computer Congress 2000.IEEE,2000:1500-1506.
KIM K,POLYDOROS A.Digital modulation classification:the BPSK versus QPSK case//MILCOM 88,21st Century Military Communications-What’s Possible?’. Conference record. Military Communications Conference. IEEE,1988:431-436.
GULDEMIR H,SENGUR A.Comparison of clustering algorithms for analog modulation classification.Expert Systems with Applications,2006,30(4):642-649.
WONG M L D,NANDI A K.Automatic digital modulation recognition using artificial neural network and genetic algorithm.Signal Processing,2004,84(2):351-365.
ZHANG X,GE T.Automatic Modulation Recognition of Communication Signals Based on Instantaneous Statistical Characteristics and SVM Classifier//2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP).IEEE,2018:344-346.
ASLAM M W,ZHU Z,NANDI A K.Automatic modulation classification using combination of genetic programming and KNN.IEEE Transactions on Wireless Communications,2012,11(8):2742-2750.
O’SHEA T J,CORGAN J,CLANCY T C.Convolutional radio modulation recognition networks//International Conference on Engineering Applications of Neural Networks.Springer,Cham,2016:213-226.
O’SHEA T J,HITEFIELD S,CORGAN J.End-to-end radio traffic sequence recognition with recurrent neural networks//2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).IEEE,2016:277-281.
TANG B,TU Y,ZHANG Z,et al.Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks.IEEE Access,2018,6:15713-15722.
HARTIGAN J A,WONG M A.Algorithm AS 136:A k-means clustering algorithm.Journal of the Royal Statistical Society.Series C (Applied Statistics),1979,28(1):100-108.
HOCHREITER S,SCHMIDHUBER J.Long short-term memory.Neural Computation,1997,9(8):1735-1780.
O’SHEA T,HOYDIS J.An introduction to deep learning for the physical layer.IEEE Transactions on Cognitive Communications and Networking,2017,3(4):563-575.
[1] WANG Rui-ping, JIA Zhen, LIU Chang, CHEN Ze-wei, LI Tian-rui. Deep Interest Factorization Machine Network Based on DeepFM [J]. Computer Science, 2021, 48(1): 226-232.
[2] YU Wen-jia, DING Shi-fei. Conditional Generative Adversarial Network Based on Self-attention Mechanism [J]. Computer Science, 2021, 48(1): 241-246.
[3] TONG Xin, WANG Bin-jun, WANG Run-zheng, PAN Xiao-qin. Survey on Adversarial Sample of Deep Learning Towards Natural Language Processing [J]. Computer Science, 2021, 48(1): 258-267.
[4] DING Yu, WEI Hao, PAN Zhi-song, LIU Xin. Survey of Network Representation Learning [J]. Computer Science, 2020, 47(9): 52-59.
[5] HE Xin, XU Juan, JIN Ying-ying. Action-related Network:Towards Modeling Complete Changeable Action [J]. Computer Science, 2020, 47(9): 123-128.
[6] YE Ya-nan, CHI Jing, YU Zhi-ping, ZHAN Yu-liand ZHANG Cai-ming. Expression Animation Synthesis Based on Improved CycleGan Model and Region Segmentation [J]. Computer Science, 2020, 47(9): 142-149.
[7] DENG Liang, XU Geng-lin, LI Meng-jie, CHEN Zhang-jin. Fast Face Recognition Based on Deep Learning and Multiple Hash Similarity Weighting [J]. Computer Science, 2020, 47(9): 163-168.
[8] BAO Yu-xuan, LU Tian-liang, DU Yan-hui. Overview of Deepfake Video Detection Technology [J]. Computer Science, 2020, 47(9): 283-292.
[9] YUAN Ye, HE Xiao-ge, ZHU Ding-kun, WANG Fu-lee, XIE Hao-ran, WANG Jun, WEI Ming-qiang, GUO Yan-wen. Survey of Visual Image Saliency Detection [J]. Computer Science, 2020, 47(7): 84-91.
[10] WANG Wen-dao, WANG Run-ze, WEI Xin-lei, QI Yun-liang, MA Yi-de. Automatic Recognition of ECG Based on Stacked Bidirectional LSTM [J]. Computer Science, 2020, 47(7): 118-124.
[11] LIU Yan, WEN Jing. Complex Scene Text Detection Based on Attention Mechanism [J]. Computer Science, 2020, 47(7): 135-140.
[12] ZHANG Zhi-yang, ZHANG Feng-li, TAN Qi, WANG Rui-jin. Review of Information Cascade Prediction Methods Based on Deep Learning [J]. Computer Science, 2020, 47(7): 141-153.
[13] JIANG Wen-bin, FU Zhi, PENG Jing, ZHU Jian. 4Bit-based Gradient Compression Method for Distributed Deep Learning System [J]. Computer Science, 2020, 47(7): 220-226.
[14] CHEN Jin-yin, ZHANG Dun-Jie, LIN Xiang, XU Xiao-dong and ZHU Zi-ling. False Message Propagation Suppression Based on Influence Maximization [J]. Computer Science, 2020, 47(6A): 17-23.
[15] CHENG Zhe, BAI Qian, ZHANG Hao, WANG Shi-pu and LIANG Yu. Improving Hi-C Data Resolution with Deep Convolutional Neural Networks [J]. Computer Science, 2020, 47(6A): 70-74.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .