Hewei Li
ABSTRACT
With the development of communication technology, the modulation methods of wireless signals show a diversified trend. Modulation pattern recognition is a very key technology in non-cooperative communication systems such as wireless signal spectrum resource regulation and modern military warfare. When performing wireless signal modulation pattern recognition, the type and quantity of the data set have an important impact on the recognition result, so it is also very important to select or construct a data set reasonably. This article mainly studies the wireless signal dataset and modulation pattern recognition technology. Firstly, provide an overview of wireless signal datasets and introduce the types and construction of wireless signal datasets. Subsequently, the principle of modulation pattern recognition is introduced and the research status of three types of modulation recognition methods, namely, likelihood ratio recognition method based on decision theory, modulation pattern recognition technique based on feature extraction and modulation recognition technique based on deep learning, is elaborated. And compare the performance of various modulation recognition technologies. Finally, a summary and outlook were made on future research directions.
- Zhitao Huang,Jie Yang,Xiang Wang,Xuan Cui,Yongfang Wang. Research progress of modulation identification algorithm in non-cooperative communication [J]. Science and Technology Herald,2019,37(04):55-62.Google Scholar
- Zebarjadi M, Teimouri M. Non‐cooperative burst detection and synchronisation in downlink TDMA‐based wireless communication networks [J]. IET Communications, 2019, 13(7): 863-872.Google ScholarDigital Library
- Weaver C S, Cole C A, Krumland R B, THE AUTOMATIC CLASSIFICATION OF MODULATION TYPES BY PATTERN RECOGNITION [R]. Stanford Univ Calif Stanford Electronics Labs, 1969.Google Scholar
- Mao Q, Hu F, Hao Q. Deep learning for intelligent wireless networks: A comprehensive survey [J]. IEEE Communications Surveys & Tutorials, 2018, 20(4): 2595-2621.Google ScholarDigital Library
- O'shea T J, West N. Radio machine learning dataset generation with gnu radio [C]//Proceedings of the GNU Radio Conference. 2016, 1(1).Google Scholar
- Tekbıyık K, Ekti A R, Görçin A, 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.Google Scholar
- O'Shea T J, Roy T, Clancy T C. Over-the-air deep learning based radio signal classification [J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 168-179.Google ScholarCross Ref
- ZHANG Shupeng, CHEN Xiaofeng, LU Zhiyi, CHEN Xuemei, SUN Jinlong, GUI Guan. Construction of real RF signal dataset for variable channel environment [J]. Radio Communication Technology,2023,49(02):248-254.Google Scholar
- Elsoufi M T E A, Ying X, Jun W, A new approach for high order MQAM signal modulation recognition [C]//Eighth International Conference on Digital Image Processing (ICDIP 2016). SPIE, 2016, 10033: 1036-1040.Google Scholar
- WU Hao,ZHOU Liang,LI Yaxing Modulation recognition method based on convolutional neural network and sparse filtering [J]. Systems Engineering and Electronics,2019,41(09):2114-2121.Google Scholar
- Liu Mingqian, Li Bingbing, Cao Chaofeng Digital modulated signal identification method under non-Gaussian noise in cognitive radio [J]. Journal of Communication,2014,35(01):82-88.Google Scholar
- Panagiotou P, Anastasopoulos A, Polydoros A. Likelihood ratio tests for modulation classification [C]//MILCOM 2000 Proceedings. 21st Century Military Communications. Architectures and Technologies for Information Superiority (Cat. No. 00CH37155). IEEE, 2000, 2: 670-674.Google Scholar
- Zhao Z, Yang A, Guo P, A modulation format identification method based signal amplitude sorting and ratio calculation [J]. Optics Communications, 2020, 470: 125819.Google ScholarCross Ref
- Huan C Y, Polydoros A. Likelihood methods for MPSK modulation classification [J]. IEEE Transactions on Communications, 1995, 43(2/3/4): 1493-1504.Google ScholarCross Ref
- ZHANG Cheng-chang, YU Shan, XU Yu, LUO Yuan. A review on the application of artificial neural network in modulation recognition [J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition),2022,34(02):181-192.Google Scholar
- ZHANG Qin,GONG Xiaofeng,LU Ruisen,DU Miao. Second-order modulation recognition model based on higher-order cumulative features [J]. Computer Simulation, 2021,38(12):454-459+464.Google Scholar
- Li D, Yang R, Dong R, Emitter signals modulation recognition based on discriminative projection and collaborative representation [J]. IET Radar, Sonar & Navigation, 2020, 14(5): 782-791.Google ScholarCross Ref
- Tan Xiaoheng,Chu Guoxing,Zhang Xuejing Modulation recognition algorithm based on higher-order cumulants and wavelet transform [J]. Systems Engineering and Electronics,2018,40(01):171-177.Google Scholar
- Jiao Xiang, Wei Xianglin, Xue Yu, Wang Chao, Duan Qiang. Research on automatic modulation recognition based on deep learning [J]. Computer Science,2022,49(05):266-278.Google Scholar
- Liu Y, Liu Y. Modulation recognition with pre‐denoising convolutional neural network [J]. Electronics Letters, 2020, 56(5): 255-257.Google ScholarCross Ref
- Li R, Song C, Song Y, Deep geometric convolutional network for automatic modulation classification [J]. Signal, Image and Video Processing, 2020, 14: 1199-1205.Google ScholarCross Ref
- T.S. Cui, K. Cui, Y.H. Huang, W.J. Zhao, J.S. An. Convolutional neural network satellite signal automatic modulation recognition algorithm [J]. Journal of Beijing University of Aeronautics and Astronautics,2022,48(06):986-994.DOI:10.13700/j.bh.1001-5965.2020.0711.Google ScholarCross Ref
- Mao Q, Hu F, Hao Q. Deep learning for intelligent wireless networks: A comprehensive survey [J]. IEEE Communications Surveys & Tutorials, 2018, 20(4): 2595-2621.Google ScholarDigital Library
- B.W. Qin, L. Jiang, H. Xu, Z.S. Qi. Modulation recognition algorithm based on residual generation adversarial network [J]. Systems Engineering and Electronics,2022,44(06):2019-2026.Google Scholar
Index Terms
- Research on wireless signal dataset and modulation pattern recognition technology
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