Multi-angle Development Analysis for Automatic Modulation Classification Technology
Authors: 
Bolin Zhang, Gang JiAuthors Info & Claims
Bolin Zhang, Gang JiAuthors Info & ClaimsPages 276 - 281
Abstract
Modulation recognition is an important part of signal perception and a key step towards intelligent perception. With the continuous development of communication technology, it has already entered the era of automatic modulation classification (AMC) from manual modulation recognition. Aiming at the signal modulation recognition technology, this article summarizes the value of modulation recognition technology in military applications and the development process of the technology. According to the modulation recognition method, It is mainly divided into the recognition method based on the likelihood function and the recognition method based on feature extraction, and the two categories are divided in detail. Explains how deep learning is applied to modulation recognition technology and what advantages it has. The recognition rate of various methods under different signal-to-noise ratio(SNR) conditions, as well as the advantages and disadvantages of various methods are summarized. The new challenges that may be encountered for modulation recognition technology are analyzed, and the future development of the modulation recognition is prospected.
References
[1]
P. A. J. Nagy, "Modulation classification --- An unified view," 1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996, pp. 1--4.
[2]
W. A. Gardner, Exploitation of Spectral Redundancy in Cydostation Signals, Signal Processing Magazine, IEEE, 1991, 4, 8(2)
[3]
Polydoros, A, Kim, K.On the detection and classification foquadrature digital modulations in broadband noise. IEEE Trans. Commun. 1990, 38, 1199--1211.
[4]
K Kim, A Polydors. Digtal Modulation Classifiction: the BPSK versus QPSK case [C]. Military Communications Conference, MILCOM 88, Conference record 1988(2): 431--436.
[5]
Huang C Y, Polydoros A. Advanced Methods for Digital Quadrature and Offset Modulation Classification[J]. Military Communications Conference, IEEE, 1991, 2:841--845
[6]
Freund Y, R E Schapire. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1):199--139
[7]
A.K. Nandi, E.E. Azzouz. Automatic identification of digital modulation types[J]. Signal Processing, 1995, 47:55--69.
[8]
Salman Hassanpour, etc Automatic Digital Modulation Recognition Based on Novel Features and Support Vector Machine[C]. 12th International Conference on Signal-Image Technology &Internet-Based Systems. 2016: 172--177.
[9]
H. Ren, J. L. Yu, Z. X. Wang, J. Chen and C. Y. Yu, "Modulation format recognition in visible light communications based on higher order statistics," in Proc. 2017 Conference on Lasers and Electro-Optics Pacific Rim, Singapore, Jul. 2017, pp.1--2.
[10]
A. Ali and Fan Yang yu, "Higher-order statistics based modulation classification using hierarchical approach," 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2016, pp. 370--374.
[11]
W. Su, "Feature Space Analysis of Modulation Classification Using Very High-Order Statistics," in IEEE Communications Letters, vol. 17, no. 9, pp. 1688--1691, September 2013.
[12]
M. R. Mirarab and M. A. Sobhani, "Robust modulation classification for PSK /QAM/ASK using higher-order cumulants," 2007 6th International Conference on Information, Communications & Signal Processing, 2007, pp. 1--4.
[13]
L. Wang and Y. B. Li, "Constellation based signal modulation recognition for MQAM," in Proc.2017 IEEE 9th International Conference on Communication Software and Networks, Guangzhou, China, May.2017, pp. 826--829.
[14]
Fen Wang, Yongchao Wang, Xi Chen. Graphic Constellations and DBN based Aut omnatic Modulation Cla sifcation[C]. IEEE 85th Vehicular Technology Conference (V TC Spring), 2017.
[15]
C. Park, J. Choi, S. Nah, W. Jang and D. Y. Kim, "Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM," 2008 10th International Conference on Advanced Communication Technology, 2008, pp. 387--390.
[16]
T. Cai, C. Wang, G. Cui and W. Wang, "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), 2015, pp. 212--216.
[17]
DobreO A, Oner M, Rajan S, et al. Cyclostationarity-based robust algorithms for qam signal identification [J]. IEEE Communications Letters, 2012, 16(1): 12--15.
[18]
Hui Wang LiLi Guo. A N ew Method of Automatic Modulation Recognition B ased on Dimension Reduction[C] 2017 Forum on Cooperative Positioning and S ervice (CPGPS). 2017: 316--320.
[19]
O'Shea, Johnathan Corgan, et al. Convolutional Radio Modulation Recognition Networks [J]. International Conference on Engineering Applications of Neural Networks, 2016: 213--226.
[20]
Xiaolei Zhu, Yun Lin, Zheng Dou. Automatic recognition of communication signal modulation based on neural network[C]. IEEE International Conference 0n E1ectronic Information and Communication Technology (ICEICT). 2016: 223--226.
[21]
Peng Shengliang, Jiang Hanyu, Wang huaxia, et al. Modulation on classification using convolutional n eural network based deep learning model[C]// Wireless and Optical Comm unication Conference. Piscataway, NJ: EEE, 2017: 1--5.
[22]
Hong D, Zhang Z, Xu X Automatic modulation classification using recurrent neural networks [C].2017 3rd IEEE International Conference on Computer and Communications(ICCC), Chengdu, China, 2017
[23]
Sinjin Jeong, Uhyeon Lee, Suk Chan Kim. Spectrogram-Based Automatic Modulation Recognition U sing Convolutional Neural Network[C]. 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), 2018, 16: 843--845
[24]
Timothy J. O'Shea, Nathan West. Radio Machine Learning Dataset Generation with GNU Ra dio[C]. Proceedings of the 6th GNU Radio Conference. 2016:1--9.
[25]
Liu Xiaoyu, Yang Diyu, Gamal A E Deep neural network architectures for modulation on classification[C]// 2017 51st Asilomar Conference on Signals, Systems, and Computers. Piscataway, NJ: EEE, 2017: 915--919.
[26]
Timothy J. O'Shea, Nathan E West. Semi-Supervised Radio Signal Identification[J]. arXiv preprint arXiv: 1611.00303v2 [cs.LG] 17 Jan 2017
[27]
K. Bu, Y. He, X. Jing and J. Han, "Adversarial Transfer Learning for Deep Learning Based Automatic Modulation Classification," in IEEE Signal Processing Letters, vol. 27, pp. 880--884, 2020.
[28]
L. Huang, W. Pan, Y. Zhang, L. Qian, N. Gao and Y. Wu, "Data Augmentation for Deep Learning-Based Radio Modulation Classification," in IEEE Access, vol. 8, pp. 1498--1506, 2020.
Index Terms
- Multi-angle Development Analysis for Automatic Modulation Classification Technology
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October 2021
1723 pages
ISBN:9781450384322
DOI:10.1145/3501409
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Published: 31 December 2021
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EITCE 2021
EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering
October 22 - 24, 2021
Xiamen, China
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EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;
Overall Acceptance Rate 508 of 972 submissions, 52%
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