Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 283-288.doi: 10.11896/JsJkx.190800072

• Computer Network • Previous Articles     Next Articles

Deep Learning Based Modulation Recognition Method in Low SNR

CHEN Jin-yin, CHENG Kai-hui 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 under Grant (LY19F020025),the MaJor Special Funding for “Science and Technology Innovation 2025” in Ningbo under Grant (2018B10063) and Signal Recognition Based on GAN ,Deep Learning for Enhancement Recognition ProJect,and Engineering Research Center of Cognitive Healthcare of ZheJiang Province under Grant (2018KFJJ07).

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Abstract: Modulation recognition of radio signals is the intermediate step of signal detection and demodulation.The existing research shows that deep learning technology can effectively identify the modulation types of radio signals.As for the low signal-to-noise ratio,there is still no good solution to the problem of the sharp drop in recognition accuracy.Inspired by the noise reduction in image fields,a modulation recognition method based on deep learning in Low SNR was proposed in this paper.It realizes the denoising of the low signal-to-noise ratio signals and solves the problem of the sharp drop.Through a large number of experiments in the open source datasets,the effectiveness of the proposed method was verified.The recognition accuracy of the low signal-to-noise ratio signals increased from 10% to 15%.Finally,we analyze the problems existing in the method and look forward to future research.

Key words: Automatic recognition of modulation type, Deep learning, Denoising model, Low signal-to-noise ratio

CLC Number: 

  • TP29
[1] TENG W L,ZHAO M S,HU M.A Novel Method for Classification of MPSK Signals.Telecommunication Engineering,2004,44(2):47-49.
[2] GIRSHICK R B.Fast R-CNN//International Conference on Computer Vision.2015:1440-1448.
[3] VINCENT P,LAROCHELLE H,BENGIO Y,et al.Extracting and composing robust features with denoising autoencoders//International Conference on Machine Learning.2008:1096-1103.
[4] TIAN C,XU Y,FEI L,et al.Deep learning for image denoising:a survey//International Conference on Genetic and Evolutionary Computing.Springer,Singapore,2018:563-572.
[5] ALI A,YANGYU F,LIU S,et al.Automatic modulation classification of digital modulation signals with stacked autoencoders.Digital Signal Processing,2017:108-116.
[6] RAMJEE S,JU S,YANG D,et al.Fast Deep Learning for Automatic Modulation Classification.arXiv https://arxiv.org/abs/1901.05850.
[7] HOCHREITER S,SCHMIDHUBER J.Long short-term memory.Neural Computation,1997,9(8):1735-1780.
[8] HU Y Q,LIU J,TAN X H.Digital modulation recognition based on instantaneous information.The Journal of China Universities of Posts and Telecommunications,2010,17(3):52-90.
[9] TENG X,TIAN P,YU H.Modulation classification based on spectral correlation and SVM//2008 4th International Conference on Wireless Communications,Networking and Mobile Computing.IEEE,2008:1-4.
[10] FEHSKE A,GAEDDERT J,REED J H.A new approach to signal classification using spectral correlation and neural networks//First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks,2005.DySPAN 2005.IEEE,2005:144-150.
[11] WU H,SAQUIB M,YUN Z,et al.Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels.IEEE Transactions on Wireless Communications,2008,7(8):3098-3105.
[12] DOBRE O A,BAR-NESS Y,SU W.Higher-order cyclic cumulants for high order modulation classification//IEEE Military Communications Conference,2003.MILCOM 2003.IEEE,2003,1:112-117.
[13] AVCI E,HANBAY D,VAROL A.An expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System for digital modulation recognition.Expert Systems with Applications,2007,33(3):582-589.
[14] ZENG D,XIONG H,WANG J,et al.An Approach to IntraPulse Modulation Recognition Based on the Ambiguity Function.Circuits,Systems,and Signal Processing,2010,29(6):1103-1122.
[15] LIU A S,ZHU Q.Automatic modulation classification based on the combination of clustering and neural network.The Journal of China Universities of Posts and Telecommunications,2011,18(4):13-38.
[16] GULDEM R H,SENGUR A.Comparison of clustering algorithms for analog modulation classification.Expert Systems with Applications,2006,30(4):642-649.
[17] 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.
[18] WANG X,GAO Z,FANG Y,et al.A Signal Modulation Type Recognition Method Based on Kernel PCA and Random Forest in Cognitive Network//International Conference on Intelligent Computing.2014:522-528.
[19] PHAM V,BLUCHE T,KERMORVANT C,et al.Dropout Improves Recurrent Neural Networks for Handwriting Recognition//International Conference on Frontiers in Handwriting Recognition.2014:285-290.
[20] LIU Q J,CHEN G M,LIU X F,et al.Application of FFT and Wavelet in Signal Denoising.Journal of Data Acquisition & Processing,2009,24(S1):58-60.
[21] DONOHO D L.De-noising by soft-thresholding.IEEE, Trans.on Inf.Theory,1995,41(3):613-627.
[22] DONOHO D L,JOHNSTONE I M.Ideal spatial adaptation by wavelet shrinkage.Biometrika,1994,81:425-455.
[23] DONOHO D L,JOHNSTONE I M.Ideal de-noising in an orthonormal basis chosen from a library of bases.Comptes Rendus de l Académie des Sciences-Series I-athematics,1994,319:1317-1322.
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