Margin-Based Active Online Learning Techniques for Cooperative Spectrum Sharing in CR Networks
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In this paper, we consider a problem of acquiring accurate spectrum availability information in the Cooperative Spectrum Sensing (CSS) based Cognitive Radio Networks (CRNs), where a fusion center collects the sensing information from all the sensing nodes within the network, analyzes the information and determines the spectrum availability. Although Machine Learning (ML) techniques have been recently applied to enhance the cooperative sensing performance in CRNs, they are mostly supervised learning based techniques and need a significant amount of labeled data, which is difficult to acquire in practice. Towards relaxing this requirement of large labeled data of supervised learning, we focus on Active Learning (AL), where the fusion center can query the label of the most uncertain cooperative sensing measurements. This is particularly relevant in CRN environments where primary user behavior changes in a quick manner. In this regard, we briefly review the existing AL techniques and adapt them to the considered CSS based CRNs. More importantly, we propose a novel margin based active on-line learning algorithm that selects the instance to be queried and updates the classifier by using the Stochastic Gradient Descent (SGD) technique. In this approach, whenever an unlabeled instance is presented, the proposed AL algorithm compares the margin of instance with a threshold to decide whether it should query a label or not. Supporting results based on numerical simulations show that the proposed method has significant advantages on classification and detection performances, and time-complexity as compared to state-of-the-art techniques.
KeywordsActive learning Cooperative spectrum sensing Cognitive radio network
This work has received partial funding from the European Research Council (ERC) under the European Union’s Horizon H2020 research and innovation programme (grant agreement No 742648), and from the Luxembourg National Research Fund (FNR) in the framework of the AFR research grant entitled “Learning-Assisted Cross-Layer Optimization of Cognitive Communication Networks”.
- 1.Zhang, L., Liang, Y., Xiao, M.: Spectrum sharing for internet of things: a survey. IEEE Wirel. Commun. 99, 1–8 (2018)Google Scholar
- 3.Lopez-Benitez, M., Casadevall, F.: Spectrum occupancy in realistic scenarios and duty cycle model for cognitive radio. Adv. Electron. Telecommun. 1(1), 26–34 (2010)Google Scholar
- 9.Zhang, D., Zhai, X.: SVM-based spectrum sensing in cognitive radio. In: International Conference on Wireless Communications, Networking and Mobile Computing (WICOM), Wuhan, China (2011)Google Scholar
- 14.Cesa-Bianchi, N., Gentile, C., Zaniboni, L.: Worst-case analysis of selective sampling for linear-threshold algorithms. In: Advances in Neural Information Processing Systems Conference (2004)Google Scholar