基于深度学习的调制识别综述

doi:10.11959/j.issn.1000-0801.2021114

Abstract

Abstract:

Modulation recognition is one of the fundamental tasks for communications systems, which can be widely applied in various fields, such as cognitive radio, intelligent communications, radio surveillance, electronic warfare, etc.In recent years, deep learning (DL) based modulation recognition has attracted great attention due to its superiority in feature extraction and recognition performance.The techniques of DL based modulation recognition were systematically summarized.Firstly, some knowledge relevant to DL based modulation recognition was introduced.Then, the system architecture, data pre-processing methods, deep neural network structures, prevalent datasets and performance metrics of DL based modulation recognition were illustrated.Finally, the future directions of DL based modulation recognition were also discussed.

Key words: deep learning, modulation recognition, data pre-processing, deep neural network

CLC Number:

TN929

Shujun SUN, Shengliang PENG, Yudong YAO, Xi YANG. A survey of deep learning based modulation recognition[J]. Telecommunications Science, 2021, 37(5): 82-90.

Figures/Tables 3

References 37

[1]	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.
[2]	PENG S L , JIANG H Y , WANG H X ,et al. Modulation classification based on signal constellation diagrams and deep learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019,30(3): 718-727.
[3]	PANAGIOTOU P , ANASTASOPOULOS A , POLYDOROS A . Likelihood ratio tests for modulation classification[C]// Proceedings of MILCOM 2000 Proceedings.21st Century Military Communications,Architectures and Technologies for Information Superiority (Cat.No.00CH37155). Piscataway: IEEE Press, 2000: 670-674.
[4]	SHI Q H , KARASAWA Y . Automatic modulation identification based on the probability density function of signal phase[J]. IEEE Transactions on Communications, 2012,60(4): 1033-1044.
[5]	XIE X J , NI Y Q , PENG S L ,et al. Deep learning based automatic modulation classification for varying SNR environment[C]// Proceedings of 2019 28th Wireless and Optical Communications Conference (WOCC). Piscataway: IEEE Press, 2019: 1-5.
[6]	董鑫, 欧阳喜, 袁强 . 多径信道下基于高阶累积量的通信信号调制识别算法[J]. 信息工程大学学报, 2015,16(1): 73-78.
	DONG X , OUYANG X , YUAN Q . Automatic modulation classification using cumulant features for communications via multipath channels[J]. Journal of Information Engineering University, 2015,16(1): 73-78.
[7]	PAWAR S U , DOHERTY J F . Modulation recognition in continuous phase modulation using approximate entropy[J]. IEEE Transactions on Information Forensics and Security, 2011,6(3): 843-852.
[8]	XIE L J , WAN Q . Automatic modulation recognition for phase shift keying signals with compressive measurements[J]. IEEE Wireless Communications Letters, 2018,7(2): 194-197.
[9]	CHIKHA W B , DAYOUB I , HAMOUDA W ,et al. Modulation recognition for MIMO relaying broadcast channels with direct link[J]. IEEE Wireless Communications Letters, 2014,3(1): 50-53.
[10]	XIE L J , WAN Q . Automatic modulation recognition for phase shift keying signals with compressive measurements[J]. IEEE Wireless Communications Letters, 2018,7(2): 194-197.
[11]	KHARBECH S , DAYOUB I , ZWINGELSTEIN-COLIN M ,et al. On classifiers for blind feature-based automatic modulation classification over multiple-input–multiple-output channels[J]. IET Communications, 2016,10(7): 790-795.
[12]	ASLAM M W , ZHU Z C , NANDI A K . Automatic modulation classification using combination of genetic programming and KNN[J]. IEEE Transactions on Wireless Communications, 2012,11(8): 2742-2750.
[13]	GOODFELLOW I J , BENGIO Y , COURVILLE A C . Deep Learning[M]. Massachusetts: MIT Press, 2016.
[14]	LEE J , KIM B , KIM J ,et al. Deep neural network-based blind modulation classification for fading channels[C]// Proceedings of 2017 International Conference on Information and Communication Technology Convergence (ICTC). Piscataway: IEEE Press, 2017: 551-554.
[15]	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.
[16]	HU S S , PEI Y Y , LIANG P P ,et al. Deep neural network for robust modulation classification under uncertain noise conditions[J]. IEEE Transactions on Vehicular Technology, 2020,69(1): 564-577.
[17]	ZHENG S L , QI P H , CHEN S C ,et al. Fusion methods for CNN-based automatic modulation classification[J]. IEEE Access, 2019(7): 66496-66504.
[18]	RAJENDRAN S , MEERT W , GIUSTINIANO D ,et al. Deep learning models for wireless signal classification with distributed low-cost spectrum sensors[J]. IEEE Transactions on Cognitive Communications and Networking, 2018,4(3): 433-445.
[19]	HONG S , ZHANG Y B , WANG Y ,et al. Deep learning-based signal modulation identification in OFDM systems[J]. IEEE Access, 2019(7): 114631-114638.
[20]	MOSSAD O S , ELNAINAY M , TORKI M . Deep convolutional neural network with multi-task learning scheme for modulations recognition[C]// Proceedings of 2019 15th International Wireless Communications ＆ Mobile Computing Conference (IWCMC). Piscataway: IEEE Press, 2019: 1644-1649.
[21]	KHAN F N , ZHONG K P , AL-ARASHI W H ,et al. Modulation format identification in coherent receivers using deep machine learning[J]. IEEE Photonics Technology Letters, 2016,28(17): 1886-1889.
[22]	O’SHEA T J , CORGAN J , CLANCY T C . Convolutional radio modulation recognition networks[C]// Proceedings of Engineering Applications of Neural Networks.[S.l.:s.n.], 2016.
[23]	HUANG S , CHAI L , LI Z N ,et al. Automatic modulation classification using compressive convolutional neural network[J]. IEEE Access, 2019(7): 79636-79643.
[24]	WANG D S , ZHANG M , LI Z ,et al. Modulation format recognition and OSNR estimation using CNN-based deep learning[J]. IEEE Photonics Technology Letters, 2017,29(19): 1667-1670.
[25]	DAVIDSON K L , GOLDSCHNEIDER J R , CAZZANTI L ,et al. Feature-based modulation classification using circular statistics[C]// Proceedings of IEEE MILCOM 2004. Piscataway:IEEE Press, 2004: 765-771.
[26]	MENDIS G J , WEI J , MADANAYAKE A . Deep learning-based automated modulation classification for cognitive radio[C]// Proceedings of2016 IEEE International Conference on Communication Systems (ICCS). Piscataway: IEEE Press, 2016: 1-6.
[27]	MA J T , LIN S C , GAO H J ,et al. Automatic modulation classification under non-Gaussian noise: a deep residual learning approach[C]// Proceedings of ICC 2019 - 2019 IEEE International Conference on Communications (ICC). Piscataway: IEEE Press, 2019: 1-6.
[28]	LI Y B , SHAO G P , WANG B . Automatic modulation classification based on bispectrum and CNN[C]// Proceedings of2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). Piscataway: IEEE Press, 2019: 311-316.
[29]	TU Y , LIN Y . Deep neural network compression technique towards efficient digital signal modulation recognition in edge device[J]. IEEE Access, 2019(7): 58113-58119.
[30]	HINTON G E , OSINDERO S , TEH Y W . A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006,18(7): 1527-1554.
[31]	MENDIS G J , WEI-KOCSIS J , MADANAYAKE A . Deep learning based radio-signal identification with hardware design[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019,55(5): 2516-2531.
[32]	WANG F , WANG Y C , CHEN X . Graphic constellations and DBN based automatic modulation classification[C]// Proceedings of 2017 IEEE 85th Vehicular Technology Conference (VTC Spring). Piscataway: IEEE Press, 2017: 1-5.
[33]	WANG Q , DU P F , YANG J Y ,et al. Transferred deep learning based waveform recognition for cognitive passive radar[J]. Signal Processing, 2019,155: 259-267.
[34]	TANG B , TU Y , ZHANG Z Y ,et al. Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks[J]. IEEE Access, 2018,6: 15713-15722.
[35]	GREFF K , SRIVASTAVA R K , KOUTNíK J ,et al. LSTM: a search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017,28(10): 2222-2232.
[36]	ZHANG M , ZENG Y , HAN Z D ,et al. Automatic modulation recognition using deep learning architectures[C]// Proceedings of2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Piscataway:IEEE Press, 2018: 1-5.
[37]	O'SHEA T J , WEST N . Radio machine learning dataset generation with GNU radio[Z]. 2016.

Metrics

Recommended 0

No Suggested Reading articles found!

文献	预处理	DNN	候选调制方案	SNR/dB	平均Acc	复杂度
Mendis等^[26]	图像	DBN	4FSK、16QAM、BPSK、QPSK、OFDM	-2～5	94.50%	/
Wang等^[32]	图像	DBN	BPSK、QPSK、8PSK、16QAM	0～10	97.57%	O(N)*
Peng等^[2]	图像	CNN	BPSK、QPSK、OQPSK、8PSK、16QAM、64QAM、4ASK	0～10	84.15%	6.6 ms
					89.00%	18 ms
Li等^[28]	图像	CNN	BPSK、2ASK、2FSK、4FSK、8FSK、LFM、OFDM	-3～10	56.35%	/
Tang等^[34]	图像	CNN	BPSK、4ASK、QPSK、OQPSK、8PSK、16QAM、32QAM、64QAM	-6～14	91.75%	/
		GAN
Tu和Lin^[29]	图像	CNN	4ASK、BSPK、QPSK、OQPSK、8PSK、16QAM、32QAM、64QAM	-6～6	91.70%	19.42 ms
		剪枝CNN			91.00%	6.7 ms
O’Shea等^[15]	序列	CNN	RadioML2018.01a	-20～20	36.50%	/
					42.00%	14 h*
Ma等^[27]	谱图	CNN	BPSK、2ASK、4QAM、8ASK、16QAM	-5～10	66.00%	/
Zheng等^[17]	序列	CNN	BPSK、QPSK、8PSK、OQPSK、2FSK、4FSK、8FSK、16QAM、32QAM、64QAM、4PAM、8PAM	-20～30	53.50%55.00%	//
Hu等^[16]	序列	RNN	BPSK、QPSK、8PSK、16QAM	0～20	79.00%	9.2 ms
Zhang等^[36]	序列	CNN	RadioML2016.10a	-20～18	54.50%	/
	特征	RNN			55.00%	/
Rajendran等^[18]	序列	RNN	RadioML2016.10a	-20～20	56.00%	6.4 MB*
注：O(N)为计算复杂度，14 h为训练时间，6.4 MB为模型大小，其余均为识别时间。

A survey of deep learning based modulation recognition

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 3

References 37

Related Articles 15

Metrics

Recommended 0

[1]	Min LU, Juan HU, Xianchao ZHANG, Weijian DING, Guangxue YUE. Personalized recommendation model based on users multi-features fusion [J]. Telecommunications Science, 2023, 39(5): 101-115.
[2]	Min LU, Zehao QIN, Zhihui CHEN, Min ZHANG, Guangxue YUE. 1D-Concatenate based channel estimation DNN model optimization method [J]. Telecommunications Science, 2023, 39(4): 71-86.
[3]	Bin ZHUGE, Zhenghu YIN, Wenxue SI, Lei YAN, Ligang DONG, Xian JIANG. Student knowledge tracking based multi-indicator exercise recommendation algorithm [J]. Telecommunications Science, 2022, 38(9): 129-143.
[4]	Jie ZHOU, Bernardo Esono Esono Mikue, Xueying WANG, Huiting ZHOU, Hong LUO. PAPR optimization based on SLM and PTS algorithms in NC-OFDM systems [J]. Telecommunications Science, 2022, 38(7): 63-74.
[5]	Yan GAO, Jian SHI, Shengyu MA, Bolin MA, Guangxue YUE. DropBlock based bimodal hybrid neural network for wireless communication modulation recognition [J]. Telecommunications Science, 2022, 38(5): 75-86.
[6]	Panpan LI, Zhengxia XIE, Guangxue YUE, Xin LIU. Research progress and trends of deep learning based wireless communication receiving method [J]. Telecommunications Science, 2022, 38(2): 1-17.
[7]	Qing SHEN, Wenbin GUO, Jungang LOU, Qiangguo YU. Personalized recommendation model with multi-level latent features [J]. Telecommunications Science, 2022, 38(2): 71-83.
[8]	Zhihong CHEN, Mingxiao WANG. Application of computer vision in intelligent security [J]. Telecommunications Science, 2021, 37(8): 142-147.
[9]	Boheng TANG, Xingang CHAI. Cloud-edge collaboration based computer vision inference mechanism [J]. Telecommunications Science, 2021, 37(5): 72-81.
[10]	Shuang PENG, Xiaodong WANG, Zongju PENG, Fen CHEN. Fast QTMT partition decision based on deep learning [J]. Telecommunications Science, 2021, 37(4): 73-81.
[11]	Daoyun HU, Jin QI, Qianchun LU, Feng LI, Hongqiang FANG. Research and application of traffic engineering algorithm based on deep learning [J]. Telecommunications Science, 2021, 37(2): 107-114.
[12]	Jie ZHANG, Lihua YANG, Zenghao WANG, Bo HU, Qian NIE. A novel deep learning based time-varying channel prediction method [J]. Telecommunications Science, 2021, 37(1): 39-47.
[13]	Yuanning LI,Baifeng NING,Zhaojie DONG. Patrol image analysis framework and deep learning method for power grid [J]. Telecommunications Science, 2020, 36(8): 167-174.
[14]	Tingting ZHANG,Jianwu ZHANG,Chunsheng GUO,Huahua CHEN,Di ZHOU,Yansong WANG,Aihua XU. A survey of image object detection algorithm based on deep learning [J]. Telecommunications Science, 2020, 36(7): 92-106.
[15]	Rui GUO,Fanchun RAN. Polar codes decoding algorithm based on convolutional neural network [J]. Telecommunications Science, 2020, 36(6): 119-124.