A Review of Research on Signal Modulation Recognition Based on Deep Learning
Abstract
:1. Introduction
- (1)
- The traditional modulation identification algorithm is investigated, and the characteristics of the signal and the advantages and disadvantages of the traditional modulation identification algorithm are summarized in detail;
- (2)
- This paper introduces the application of CNN, RNN, combined neural network, and other types of neural networks in modulation recognition, and makes a comprehensive summary of new algorithms for modulation recognition based on deep learning from 2020 to 2022;
- (3)
- This paper describes the challenges faced by deep learning in identifying modulated signals in a small sample environment and summarizes the solutions to these challenges.
2. Traditional Methods of Modulation Recognition
2.1. Identification Method Based on Likelihood Ratio
2.2. Feature-Based Recognition Algorithms
3. The Modulation Recognition Method Based on Deep Learning
3.1. Application of Convolutional Neural Network in Modulation Recognition
3.1.1. Convolutional Neural Networks
3.1.2. The Modulation Recognition Method Based on Convolutional Neural Network
- 1.
- A two-dimensional image recognition method based on CNN.
- 2.
- The signal sequence recognition method based on CNN.
3.2. Application of the Recurrent Neural Network in Modulation Recognition
3.2.1. Recurrent Neural Networks
3.2.2. Modulation Recognition Based on Recurrent Neural Network
3.3. Application of Combination Neural Network in Modulation Recognition
3.4. Applications of Other Neural Networks in Modulation Recognition
4. Modulation Recognition Based on Deep Learning in Small Sample Environment
4.1. The Small Sample AMR Algorithm Based on Signal Data Enhancement
4.1.1. Generate Dummy Data
4.1.2. Data Augmentation Method Based on Semi-Supervised Learning
4.2. Small-Sample AMR Algorithm Combining Deep Learning and Transfer Learning
5. Hardware Implementation of Modulation Recognition Algorithm Based on Deep Learning
6. Research Challenges and Future Directions
6.1. Further Research on DL-Based AMR Algorithms in Visible Light Communication Systems
6.2. Improving Modulation Recognition Accuracy in Small Sample Scenarios
6.3. Strengthening the Research on Adaptive Modulation Identification
6.4. Designing more Lightweight Modulation Recognition Networks
6.5. Design the Modulation Identification Method of OFDM Signal
7. Summary
Author Contributions
Funding
Conflicts of Interest
References
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Method | Advantages | Disadvantages |
---|---|---|
Modulation identification method based on the likelihood ratio | Have a complete theoretical foundation | The derivation of the likelihood function is complicated, and the amount of calculation is large |
The classification effect is very good | Poor applicability | |
Better performance at low signal-to-noise ratio | Requires a small amount of prior knowledge Requires a lot of prior knowledge | |
Feature-based modulation identification | Simple theoretical analysis | The identification system is complex |
Features are easy to extract when the signal-to-noise ratio is high | There is no complete theoretical basis |
Author | Year | Input Signal | Model | Modulation Signal Type | Recognition Accuracy |
---|---|---|---|---|---|
D. Hong et al. [67] | 2017 | IQ signal | Two-layer GRU | WB-FM, AM-SSB, AM- DSB, BPSK, QPSK, 8PSK, 16QAM, 64QAM, BFSK, CPFSK, PAM4 | 91% (−4 dB) |
Kim, H et al. [68] | 2019 | Amplitude phase of IQ signal | LSTM | 8PSK, AM-DSB-BPSK, PFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM | 80% (6 dB) |
Wang, Yan et al. [69] | 2020 | IQ signal | HSNet | 16QAM, 32QAM, BPSK, QPSK, 8PSK, PAM, CPFSK, GFSK, FM, SSB | 86% (0 dB) |
H. Yang et al. [70] | 2021 | Polar coordinate representation of the signal | IRS + LSTM | BPSK, QPSK, 8PSK, 16QAM, 64QAM, GFSK, CPFSK, 4PAM, WBFM, AM–DSB | 90% (0 dB) |
Liu et al. [71] | 2021 | IQ signal | DCN-BiLSTM | AM-DSB, AM-SSB, WBFM, BPSK, 8PSK, QPSK, CPFSK, GFSK, PAM4, 16QAM, 64QAM | 90% (4 dB) |
Zi. et al. [72] | 2022 | IQ signal | LSTM | 8PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM | 90% (8 dB) |
V.N.Senthil Kumaran [73] | 2022 | Original signal | EDL-MSC (GRU + Bi LSTM + SSAE) | 8PSK, BPSK, 16QAM, 64QAM, QPSK | 92% (−4 dB) |
UdayaDampage [74] | 2022 | IQ signal | LSTM + Bi LSTM | BPSK, QPSK, 16QAM, 64QAM, 256QAM | 90% (0 dB) |
P. Ghasemzadeh [75] | 2022 | IQ signal | S-QRNN | OOK, 4ASK, 8ASK, BPSK, QPSK, 8PSK, 16PSK, 32PSK, 16APSK, 32APSK, 64APSK, 128APSK, 16QAM et | 90% (5 dB) |
Author | Year | Input Signal | Model | Modulation Signal Type | Recognition Accuracy |
---|---|---|---|---|---|
B. Tang et al. [81] | 2018 | Outline constellation map | CNN + GAN | 4ASK, BPSK, OQPSK, 8PSK, 16QAM, 32QAM, 64QAM | 100% (−2 dB) |
F. Liu et al. [82] | 2020 | IQ sequence, cyclic spectrum | CNN + GRU | 2PSK, 2ASK, 2FSK, 4PSK, 4ASK, 16QAM, 64QAM | 100% (0 dB) |
J. Xu et al. [83] | 2021 | IQ sequence | MCLDNN(CNN + LSTM + FC) | WBFM, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, 4-PAM, 16-QAM, 64-QAM, QPSK, and 8PSK | 90% (0 dB) |
Jiang K et al. [84] | 2021 | IQ sequence | CNN + Bi LSTM + Attention | 2ASK, 4ASK, BPSK, QPSK, 8PSK, 64QAM | 93.14% (10 dB) |
Duan, Q et al. [85] | 2021 | Original signal | MCBL (CNN + Bi LSTM + Attention) | 8PSK, AM-DSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, WBFM | 93% (0 dB) |
Z. Liang et al. [86] | 2021 | Time-frequency diagram | ResNeXt WSL(ResNeXt + Attention) | 8PSK, BPSK, AM-DSB, QPSK, QAM16, QAM64, CPFSK, GFSK, PAM4, and WBFM, | 90% (0 dB) |
S. Chang et al. [87] | 2022 | Sampled signal | MLDNN(CNN + Bi GRU + SAFN) | AM-DSB, AM-SSB, WBFM. 8PSK, BPSK, CPFSK, GFSK, 4PAM, 16QAM, 64QAM, QPSK | 84% (0 dB) |
W. Zhang et al. [88] | 2022 | Sampled signal | GRU + CNN | BPSK, QPSK, BFSK, QFSK, 16QAM, 64QAM, OFDM | 99.45% (true channel) |
Zou B et al. [89] | 2022 | IQ sequence | ASCLDNN (Attention + CLDNN) | BPSK, 8PSK, CPFSK, GFSK, PAM4, PAM16, QAM64, QPSK, AM-DSB, AM-SSB, WBFM | 90% (0 dB) |
Bai J et al. [90] | 2022 | IQ sequences and GAF images | DMFF-CNN (Complex Value Network + ResNet50) | 2FSK, AM, DSB, FM, OFDM, QAM16, QPSK, SSB | 91% (−10 dB) |
Reference A | Author | Year | Input Signal Type | Model | Recognition Accuracy | Advanced Techniques Mentioned in Reference A | Author | Year | Input Signal Type | Model | Recognition Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|
[36] | Pengshengliang et al. | 2019 | Constellation Chart | GoogleNet, AlexNet | 90% (3 dB) | [118] | Fen Wang | 2019 | Constellation Chart | GCP-DBN | 95% (0 dB) |
[40] | Zhuolun LI et al. | 2019 | Eye Chart | Improved ResNet | >95% (5 dB) | [36] | Pengshengliang | 2019 | Constellation Chart | Goog-le-Net, AlexNet | 90% (3 dB) |
[41] | Xiong Zha et al. | 2021 | Constellation and eye charts | Designed multi-input CNN | 92% (3 dB) | [36] | Pengshengliang | 2019 | Constellation Chart | Goog-le-Net, AlexNet | 90% (3 dB |
[42] | Yongjiang Mao et.al | 2021 | Time and frequency diagram | Sep-ResNet | 93.44% (−10 dB) | [119] | Zhiyu Qu | 2020 | Time and frequency diagram | CNN | 96.1% (−6 dB) |
[43] | Daying Quan et al. | 2021 | Time and frequency diagram | Dual-channel CNN | 97% (−6) | [120] | Jian Wan | 2019 | Time and frequency diagram | CNN+TPOT | 94.42% (−4 dB) |
[45] | Gao Jingpeng et al. | 2019 | Circulation spectrogram | CNN | 90.48% (−2 dB) | [121] | Xiao Yan | 2018 | Constellation and eye charts | Modulation classifier using the minimum angle search | >90% (15 dB) |
[47] | Hongjing Lv et al. | 2021 | Magnitude histogram | CNN | 100% | [122] | Zhiquan Wan | 2019 | Magnitude histogram | MTL-ANN | 100% |
[49] | Sheng Hong et al. | 2019 | IQ Signal | CNN | 98% (8 dB) | [92] | Wenwu Xie | 2019 | High-order cumulative volume | DNN | 99% (−5 dB) |
[73] | V.N.Senthil Kumaran et al. | 2022 | IQ Signal | EDL-MSC (GRU + BI LSTM+SSAE) | 92% (−4 dB) | [69] | Duona Zhang | 2020 | IQ Signal | HsNet | 86% (0 dB) |
[81] | Pejman Ghasemzadeh et al. | 2022 | IQ Signal | S-QRNN | 90% (5 dB) | [123] | Sai Huang | 2020 | Signal cyclic correntropy vector (CCV) | LSTM+DenseNet(LSMD) | 90% (−6 dB) |
[80] | Tuo Wang et al. | 2021 | IQ Signal, Constellation Chart | SCMS (CNN + IndRNN) +VCD | 100% (0 dB) | [124] | Yuan Zeng | 2019 | Spectrum diagram | SCNN, SCNN2 | 80% (0 dB) |
[79] | Judith Nkechinyere Njokuet al. | 2021 | IQ Signal | CGDNet | 90% (18 dB) | [123] | Sai Huang | 2020 | Signal cyclic correntropy vector (CCV) | LSTM + DenseNet(LSMD) | 90% (−6 dB) |
[82] | Fugang Liu et al. | 2022 | Circulation spectrogram + IQ Signal | CNN + GRU | 100% (0 dB) | [119] | Zhiyu Qu | 2020 | Time frequency diagram | CNN | 96.1% (−6 dB) |
[83] | Jialang Xu et al. | 2020 | IQ Signal | MCLDNN (CNN+LSTM + FC) | 90% (0 dB) | [125] | Erma Perenda | 2019 | IQ Signal | 1D-CNN | Close to 90% (0 dB) |
[84] | Kaiyuan Jiang et al. | 2021 | IQ Signal | CNN + Bi LSTM + Attention | 93.14% (10 dB) | [71] | Kai Liu | 2021 | IQ Signal | DCN-Bi-LSTM | 90% (4 dB) |
[88] | W.Zhang et al. | 2022 | Sampling signal | GRU + CNN | 99.45% (Real channel) | [126] | Zufan Zhang | 2020 | IQ Signal | CNN-LSTM | 83% (−2 dB) |
[100] | HuajiZHOU et al. | 2022 | IQ Signal | GAN + CNN | 90% (400 signal samples) | [127] | Xiaohui Yao | 2019 | Sampling signal | GAN | 92% (12,000 signal samples) |
[104] | Lixin Li et al. | 2021 | IQ Signal | AMR-CapsNet | 80% (8103 signal samples) | [128] | Sabour, S. | 2017 | Handwritten digital picture | CapsNet | 95.7% |
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Xiao, W.; Luo, Z.; Hu, Q. A Review of Research on Signal Modulation Recognition Based on Deep Learning. Electronics 2022, 11, 2764. https://doi.org/10.3390/electronics11172764
Xiao W, Luo Z, Hu Q. A Review of Research on Signal Modulation Recognition Based on Deep Learning. Electronics. 2022; 11(17):2764. https://doi.org/10.3390/electronics11172764
Chicago/Turabian StyleXiao, Wenshi, Zhongqiang Luo, and Qian Hu. 2022. "A Review of Research on Signal Modulation Recognition Based on Deep Learning" Electronics 11, no. 17: 2764. https://doi.org/10.3390/electronics11172764