Abstract
Modulation recognition is an important research branch in the field of communication, which is widely used in civil and military fields. The classic methods depend on decision theory, signal feature and the choice of classifier, while the deep learning network can get the signal feature directly from the data, and its recognition accuracy is higher than the classic methods. This paper summarized the application of deep learning in modulation recognition. Firstly, the basic concept of deep learning and the common network structure in modulation recognition were introduced. Secondly, the common signal forms and signal preprocessing technologies of input deep learning network were given, and the characteristics and performance of different deep learning networks were summarized and analyzed. Finally, the challenges and future research directions in this field were discussed.
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Abbreviations
- ACGAN:
-
Auxiliary classifier generative adversarial network
- AE:
-
Auto encoder
- AI:
-
Artificial intelligence
- AMC:
-
Automatic modulation classification
- AM-DSB:
-
Amplitude modulation-double side band
- AMR:
-
Automatic modulation recognition
- AM-SSB:
-
Amplitude modulation-single side band
- AN-SF-CNN:
-
Anti-noise processing and deep sparse filtered convolutional neural network
- ASB:
-
Amplitude spectrums of bispectrum
- ASK:
-
Amplitude-shift keying
- BFSK:
-
Binary frequency-shift keying
- BP:
-
Back-propagation
- BPSK:
-
Binary phase-shift keying
- CapsNet:
-
Capsule network
- CCNN:
-
Compressive convolutional neural network
- CGC:
-
Contrast-enhanced grid constellations images
- CNN:
-
Convolutional neural network
- ConvLSTM:
-
Convolutional long short-term memory
- CPFSK:
-
Continuous-phase frequency-shift keying
- DBN:
-
Deep belief networks
- DL:
-
Deep learning
- DNN:
-
Deep neural networks
- DT:
-
Decision tree
- FECNN:
-
Feature extraction convolutional neural network
- FFNN:
-
Feed-forward neural networks
- FFT:
-
Fast Fourier transform
- FSK:
-
Frequency-shift keying
- GAN:
-
Generative adversarial network
- GMCNN:
-
Graph mapping convolutional neural network
- GPU:
-
Graphics processing unit
- GRU:
-
Gate recurrent unit
- H-DNN:
-
Hierarchical deep neural networks
- ILSVRC:
-
ImageNet large-scale visual recognition challenge
- IQ:
-
In-phase quadrature
- IBCNN:
-
Image-based CNN
- KNN:
-
K-nearest neighbor
- LSTM:
-
Long short-term memory
- MCLDNN:
-
Multi-channel convolutional long short-term deep neural network
- ML:
-
Machine learning
- MLP:
-
Multilayer perceptron
- MTL:
-
Multi-task learning
- IBCNN:
-
Image-based CN
- NAS:
-
Neural architecture search
- AP:
-
Amplitude phase
- PAM4:
-
Four pulse amplitude modulation
- PSK:
-
Phase-shift keying
- PSO:
-
Particle swarm optimization
- QAM:
-
Quadrature amplitude modulation
- QPSK:
-
Quadrature phase-shift keying
- RBM:
-
Restricted Boltzmann machine
- RC:
-
Regular constellation images
- RNN:
-
Recurrent neural network
- SCF:
-
Spectral correlation function
- SCRNN:
-
Sequential convolutional recurrent neural networks
- SF-CNN:
-
Sparse filtered convolutional neural network
- SNR:
-
Signal to noise ratio
- SSAE:
-
Stacked sparse auto encoder
- SSRCNN:
-
Semi-supervised signal recognition convolutional neural network
- STFT:
-
Short-time Fourier transform
- SVM:
-
Support vector machine
- UniQGAN:
-
Unified generative adversarial networks
- VHF:
-
Very high frequency
- WB-FM:
-
Wide band-frequency modulation
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This work was supported by the National Natural Science Foundation of China under Grant 62171345.
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YS: conceptualization, methodology, writing—review & editing. WW: writing—original draft, visualization.
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Sun, Y., Wu, W. Survey of Research on Application of Deep Learning in Modulation Recognition. Wireless Pers Commun 133, 1483–1515 (2023). https://doi.org/10.1007/s11277-023-10826-1
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DOI: https://doi.org/10.1007/s11277-023-10826-1