Automatic Modulation Classification Based on Constellation Density Using Deep Learning

Kumar, Yogesh; Sheoran, Manu; Jajoo, Gaurav; Yadav, Sandeep

doi:10.1109/lcomm.2020.2980840

Cited by 81 publications

(35 citation statements)

References 10 publications

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“…Training the proposed network e classification accuracy of the proposed method reaches 90% at 0 dB, while the accuracy of the other methods is less than 90%. e 8 Mathematical Problems in Engineering classification accuracy of the proposed method peaks at 93.76% when the SNR is 14 dB and it is 6 percent higher than that of LSTM and 13 percent higher than that of MentorNet, Inception, and ResNet purely. e performance of the proposed method is verified by this experiment.…”

Section: Model Trained Evaluationmentioning

confidence: 94%

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Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

Sun

et al. 2020

Mathematical Problems in Engineering

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With the development of artificial intelligence technology, deep learning has been applied to automatic modulation classification (AMC) and achieved very good results. In this paper, we introduced an improved deep neural architecture for implementing radio signal identification tasks, which is an important facet of constructing the spectrum-sensing capability required by software-defined radio. The architecture of the proposed network is based on the Inception-ResNet network by changing the several kernel sizes and the repeated times of modules to adapt to modulation classification. The modules in the proposed architecture are repeated more times to increase the depth of neural network and the model’s ability to learn features. The modules in the proposed network combine the advantages of Inception network and ResNet, which have faster convergence rate and larger receptive field. The proposed network is proved to have excellent performance for modulation classification through the experiment in this paper. The experiment shows that the classification accuracy of the proposed method is highest with the varying SNR among the six methods and it peaks at 93.76% when the SNR is 14 dB, which is 6 percent higher than that of LSTM and 13 percent higher than that of MentorNet, Inception, and ResNet purely. Besides, the average accuracy from 0 to 18 dB of the proposed method is 3 percent higher than that of GAN network. It will provide a new idea for modulation classification aiming at distraction time signal.

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Section: Model Trained Evaluationmentioning

confidence: 94%

“…At present, AMC can be divided into two categories: likelihood-based (LB) and feature-based (FB) [8]. e LB modulation classifier recognizes the modulation of signal by comparing the likelihood function value of received signal within the known modulation pool [6].…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

Sun

et al. 2020

Mathematical Problems in Engineering

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“…Deep learning, as a novel development method, can extract more meaningful features through its hierarchical learning process. For modulation classification, DL-based methods can automatically learn distinctive representations of high-dimensional data, such as the received radio signals [ 21 ].…”

Section: The Proposed Frameworkmentioning

confidence: 99%

Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Han

Ren

et al. 2021

Sensors

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Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

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“…In (2), n is the number of target images captured by the camera under different viewing angles; p is the number of target feature points; m ij is the observed value of the coordinate of the j-th feature point of the i-th target image; m ij is the theoretical value of the projection point coordinate of the target feature point under the nonlinear model; M j is the spatial coordinate of the j-th feature point on the target.…”

Section: Camera Calibrationmentioning

confidence: 99%

A Sports Training Video Classification Model Based on Deep Learning

2021

Scientific Programming

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A sports training video classification model based on deep learning is studied for targeting low classification accuracy caused by the randomness of objective movement in sports training video. The camera calibration technology is used to restore the position of the target in the real three-dimensional space. After the camera calibration in the video, the sports training video is preprocessed. The input video segment is divided into equal length segments to obtain the subvideo segment. The motion vector field, brightness feature, color feature, and texture feature of the subvideo segment are extracted, and the extracted features are input into the AlexNet convolutional neural network. ReLU is used as the activation function in this convolutional neural network. Local response normalization is used to suppress and enhance the output of neurons to highlight the performance of useful information, so that the output classification results are more accurate. Event matching method is used to match the convolutional neural network output to complete the sports training video classification. The experimental results of the proposed study show that the model can effectively solve the problems of target moving randomness. The classification accuracy of sports training video is more than 99%, and the classification speed is faster which is shown from the results of the experiments.

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Automatic Modulation Classification Based on Constellation Density Using Deep Learning

Cited by 81 publications

References 10 publications

Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

A Sports Training Video Classification Model Based on Deep Learning

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