Abstract
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 well-known 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 classifier-based approaches. Hence, SigmaBoost can be used in multimedia-enabled IoT for quick discrimination of wireless signals to ensure better radio spectrum management.
Highlights
The Internet of Things (IoT) is a vision of future internet that provides connectivity ‘‘any time,’’ ‘‘any place,’’ for ‘‘any object’’
The performance of maximum likelihood (ML), support vector machine (SVM) and naïve bayes (NB) Classifiers for automatic classification of wireless signals based on their modulation schemes are presented for comparison
At low SNR SigmaBoost performs much better than KNNAdaboost achieving 70%
Summary
The Internet of Things (IoT) is a vision of future internet that provides connectivity ‘‘any time,’’ ‘‘any place,’’ for ‘‘any object’’. Likelihoodbased approaches are based on hypothesis testing, by comparing the likelihood functions of received signals to classify different modulations These decision-theoretic approaches achieve optimal performance with the cost of high computational complexity. 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]. A LST-Based classification algorithm for distributed low-powered sensors proposed in [35] outperformed the CNN model with oversampling the received signals at small or medium scales Despite their ability to learn the features automatically reported in many recent studies, the deep learning-based approaches mainly rely on specific data representation for classification barely being reported.
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