计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 283-288.doi: 10.11896/JsJkx.190800072
陈晋音, 成凯回, 郑海斌
CHEN Jin-yin, CHENG Kai-hui and ZHENG Hai-bin
摘要: 无线电信号的调制类型识别是信号检测与解调的中间步骤,已有的研究表明利用深度学习技术能高效地识别无线电信号调制类型。但对于低信噪比区间内识别准确率骤降的问题,仍没有一种较好的解决方案。受到深度学习在图像降噪中的启发,本文提出了低信噪比下基于深度学习的调制模式识别方法,实现了对低信噪比信号的降噪处理,解决了低信噪比区间信号识别准确率过低的问题。通过在开源数据集下的大量实验,验证了本方法的有效性,低信噪比信号调制类型识别的准确率由10%上升至15%。最后,文章对于本方法存在的问题进行分析,并对未来的研究进行了展望。
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[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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