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Survey of Research on Application of Deep Learning in Modulation Recognition

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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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Availability of Data and Materials

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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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Funding

This work was supported by the National Natural Science Foundation of China under Grant 62171345.

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  1. School of Telecommunications Engineering, Xidian University, Xi’an, Shaanxi, China

    Yongjun Sun & Wanting Wu

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  1. Yongjun Sun
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  2. Wanting Wu
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YS: conceptualization, methodology, writing—review & editing. WW: writing—original draft, visualization.

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Correspondence to Yongjun Sun.

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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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