Abstract
Cognitive radio nodes are motivated to perform jamming attacks, if the illegal gains exceed the payment due to the attacking cost and the loss from the resulting punishment. In this chapter, we present active anti-jamming solutions in CRNs, based on reciprocity principles, social norms and reinforcement learning techniques. We investigate how to apply reciprocity principles to exploit the requirement of network services by SUs to suppress the motivation of insider jammers. With a properly designed social norm and reputation updating process, rational secondary users choose not to block the ongoing transmissions for their own interests. In dynamic CRNs, reinforcement learning algorithms such as Q-learning and WoLF-Q can be applied to improve the anti-jamming performance of secondary users.
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Xiao, L. (2015). Active Anti-jamming Solutions in CRNs. In: Anti-Jamming Transmissions in Cognitive Radio Networks. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-24292-7_6
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DOI: https://doi.org/10.1007/978-3-319-24292-7_6
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