计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 266-278.doi: 10.11896/jsjkx.211000085
焦翔1, 魏祥麟2, 薛羽1, 王超1, 段强2
JIAO Xiang1, WEI Xiang-lin2, XUE Yu1, WANG Chao1, DUAN Qiang2
摘要： 非合作通信场景下,自动调制识别是实现频谱感知、频谱管理、频谱利用的关键一环,也是进行高效信号处理的重要前提。传统基于模式识别的AMR方法需要手工进行特征提取,面临着设计复杂性高、识别精度低、泛化能力弱等难题。为此,学术界将目光转向以提取数据中隐含特征见长的深度学习方法,提出了多种面向AMR的深度神经网络架构。相比传统方法,ADNN取得了更高的识别精度,且泛化能力更强,适用范围更广。文中对ADNN领域的研究进行了全面的梳理总结,使从业者可以更好地了解该领域的研究现状,明晰该领域存在的问题以及未来的发展方向。首先,介绍了ADNN设计中涉及的典型DL方法;其次,描述了AMR问题的内涵,简述了传统解决方案;然后,详细介绍了ADNN的工作流程、方法分类和各类方法中的典型代表;最后,在公开数据集上对代表性方案进行了实验对比,并指出了该领域未来需要重点研究的几个方向。
中图分类号:
| [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. |
| [1] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
| [2] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
| [3] | 柳杰灵, 凌晓波, 张蕾, 王博, 王之梁, 李子木, 张辉, 杨家海, 吴程楠. 基于战术关联的网络安全风险评估框架 Network Security Risk Assessment Framework Based on Tactical Correlation 计算机科学, 2022, 49(9): 306-311. https://doi.org/10.11896/jsjkx.210600171 |
| [4] | 王磊, 李晓宇. 基于随机洋葱路由的LBS移动隐私保护方案 LBS Mobile Privacy Protection Scheme Based on Random Onion Routing 计算机科学, 2022, 49(9): 347-354. https://doi.org/10.11896/jsjkx.210800077 |
| [5] | 窦家维. 保护隐私的汉明距离与编辑距离计算及应用 Privacy-preserving Hamming and Edit Distance Computation and Applications 计算机科学, 2022, 49(9): 355-360. https://doi.org/10.11896/jsjkx.220100241 |
| [6] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
| [7] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
| [8] | 蹇奇芮, 陈泽茂, 武晓康. 面向无人机通信的认证和密钥协商协议 Authentication and Key Agreement Protocol for UAV Communication 计算机科学, 2022, 49(8): 306-313. https://doi.org/10.11896/jsjkx.220200098 |
| [9] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
| [10] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
| [11] | 周连兵, 周湘贞, 崔学荣. 基于双重二维混沌映射的压缩图像加密方案 Compressed Image Encryption Scheme Based on Dual Two Dimensional Chaotic Map 计算机科学, 2022, 49(8): 344-349. https://doi.org/10.11896/jsjkx.210700235 |
| [12] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
| [13] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
| [14] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
| [15] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
|
||
首页