Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM

Park, Cheol-Sun; Choi, Jun‐Ho; Nah, Sun-Phil; Jang, Won; Kim, Dae Young

doi:10.1109/icact.2008.4493784

Cited by 95 publications

(46 citation statements)

References 4 publications

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“…FB-based algorithms provide robust performance with low complexity [17] by exploiting the signal statistical features efficiently and translating them into classification parameters. In many exiting studies different statistical features have been employed for wireless signal format classification, such as cyclic features [18], wavelet transforms [19], [20], higher-order statistics and cumulants [21]- [23]. The classification subsystem in FB-methods categorizes the correct target groups based on these distinct input features extracted from the dataset.…”

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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“…Hidden layers maybe one layer or multilayer, and each layer consists of several nodes. The [26,37] (ii) KNN [38,91] (iii) SVM [6,27,47,48,92] (iv) Naïve Bayes [39] (v) HMM [46] (vi) Fuzzy classifier [93] (vii) Polynomial classifier [40,94] (i) DNN [24,30,31,61] (ii) DBN [49,63] (iii) CNN [17, 19-21, 54, 64, 65, 70, 73-76, 79, 81, 82, 95, 96] (iv) LSTM [29,69] (v) CRBM [53] (vi) Autoencoder network [50,62] (vii) Generative adversarial networks [66,67] (viii) HDMF [71,72] (ix) NFSC [78] Pros (i) works better on small data (ii) low implementation cost (i) simple pre-processing (ii) high accuracy and efficiency (iii) adaptive to different applications Cons (i) time demanding (ii) complex feature engineering (iii) depends heavily on the representation of the data (iv) prone to curse of dimensionality (i) demanding large amounts of data (ii) high hardware cost node presented in Figure 3 is the basic operational unit, in which the input vector is multiplied by a series of weights and the sum value is fed into the activation function . These operational units contribute to a powerful network, which could realize complex functions such as regression and classification.…”

Section: Definition Of DL Problemmentioning

confidence: 99%

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Dong

Zhou

et al. 2019

Wireless Communications and Mobile Computing

112

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Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

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“…However, these feature extraction-based AMR methods are considered as instantaneous realization scenarios, such as instantaneous features, wavelet transform-based features, high-order statistics-based features, cyclic spectrum analysis-based features, and so on. To classify the modulation types by extracted features [11], [12], they usually adopt various classifiers, such as high-order cumulants (HOC), support vector machine (SVM), decision tree (DT), k-nearest neighbor (KNN) and multilayer perception (MLP). According to aforementioned discussion, one may find that here traditional AMR methods require instantaneous information and real-time computational computing.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

Shi¹,

Hong

Cai

et al. 2020

IEEE Access

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Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in realistic communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

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Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM

Cited by 95 publications

References 4 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

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

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