Deep Learning in Digital Modulation Recognition Using High Order Cumulants

Xie, W.; Hu, Sheng; Yu, Chao; Zhu, Peng; Peng, Xin; Ouyang, Jing-Cheng

doi:10.1109/access.2019.2916833

Cited by 85 publications

(26 citation statements)

References 24 publications

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“…The tremendous success of deep learning [29] in computer vision and natural language processing with its ability to learn complex features automatically, led to the development of some of very successful deep learning-based algorithms for communications systems including signal classification based on modulation schemes [30]- [33]. Convolutional Neural Networks based classification approaches were proposed in [30], [32], [34].…”

Section: A Brief Review Of Wireless Signal Classification Methodsmentioning

confidence: 99%

Automatic Wireless Signal Classification in Multimedia Internet of Things: An Adaptive Boosting Enabled Approach

et al. 2019

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Provision of multimedia contents over the Internet of things (IoT) presents significant challenges to wireless networks owing to nodes diversity. Therefore, efficient utilization of resources is required to meet the growing diversity in user's behavior and wireless services that mark the future of wireless communication. Automatic classification of wireless signals based on their modulation schemes is a crucial technology that enables wireless transceivers to utilize the resources efficiently. Traditional feature-based approaches lack generalization, and versatility by relying on single classifier predictions. In this paper, two adaptive boosting (Adaboost) based wireless signal classifiers called SigmaBoost and KNNAdaboost are proposed. Adaboost generates an optimal prediction rule by combining the prediction of many weak component classifiers (CCs). However, sometimes it overfits on real-world scenarios with noisy data. That makes the choice of CC vital for its success in classifying wireless signals. Therefore, two wellknown classifiers support vector machine (SVM) and k-nearest neighbor (KNN) are used as CCs in proposed schemes respectively. In SigmaBoost an adaptive decrementing mechanism is introduced, which decreases the value of Gaussian radial function (RBF) of SVM kernel as boosting progresses. It not only ensures the generation of weak RBFSVM component classifiers but also improves diversity across the ensemble. Signal spectral features and higher-order cumulants are used as input features. The experimental results demonstrate significant gain in the performance of SigmaBoost, as compared to KNNAdaboost and other single classifierbased approaches. Hence, SigmaBoost can be used in multimedia-enabled IoT for quick discrimination of wireless signals to ensure better radio spectrum management. INDEX TERMS Multimedia Internet of Things, support vector machine, k-nearest neighbor, adaptive boosting, wireless signal classification, higher-order cumulants.

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Section: A Brief Review Of Wireless Signal Classification Methodsmentioning

confidence: 99%

Automatic Wireless Signal Classification in Multimedia Internet of Things: An Adaptive Boosting Enabled Approach

et al. 2019

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“…Wavelet analysis is a local transform of time and frequency, which can effectively extract information from the signal and is conducive to the perception of the surrounding electromagnetic environment. For some time, many CR technologies are devoted to modulation recognition of communication signals by using spectrum and cyclic spectrum [2], characteristic parameters and their statistics [3], time-frequency transform [4], [5], and high-order cumulants [6]- [8]. However, these methods are difficult to achieve multiresolution analysis of the modulated signals, which increases the difficulty of ob-taining effective information, and the real-time performance of signal analysis and processing is not good.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…In the simulation, 500 repeated experiments were performed to analyze the recognition rate of three kinds of wavelet entropy, and the classification and recognition were carried out according to the range of the characteristic parameters corresponding to different modulation signals. Take a= [8,16,32,16,8,4] and the recognition rate is shown in Fig. 10.…”

Section: Comparison Of Wace and Traditional Wavelet Entropymentioning

confidence: 99%

Intelligent Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy in Cognitive Radio Networks

et al. 2020

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“…Current studies mainly focus on recognition methods based on statistical patterns consisting of two key steps: feature extraction and classification. Signal characteristics are extracted based on the frequency spectrum [ 4 ], high-order cumulant [ 5 ] and so on for classification.…”

Section: Introductionmentioning

confidence: 99%

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Chen

Zhang

Zhu

et al. 2021

Sensors

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Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

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Deep Learning in Digital Modulation Recognition Using High Order Cumulants

Cited by 85 publications

References 24 publications

Automatic Wireless Signal Classification in Multimedia Internet of Things: An Adaptive Boosting Enabled Approach

Automatic Wireless Signal Classification in Multimedia Internet of Things: An Adaptive Boosting Enabled Approach

Intelligent Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy in Cognitive Radio Networks

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

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