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In cognitive radio networks (CRNs), dynamic spectrum access has been proposedto improve the spectrum utilization, but it also generates spectrum misuseproblems. One common solution to these problems is to deploy monitors to detectmisbehaviors on certain channel. However, in multi-channel CRNs, it is verycostly to deploy monitors on every channel. With a limited number of monitors,we have to decide which channels to monitor. In addition, we need to determinehow long to monitor each channel and in which order to monitor, becauseswitching channels incurs costs. Moreover, the information about the misusebehavior is not available a priori. To answer those questions, we model thespectrum monitoring problem as an adversarial multi-armed bandit problem withswitching costs (MAB-SC), propose an effective framework, and design two onlinealgorithms, SpecWatch-II and SpecWatch-III, based on the same framework. Toevaluate the algorithms, we use weak regret, i.e., the performance differencebetween the solution of our algorithm and optimal (fixed) solution inhindsight, as the metric. We prove that the expected weak regret ofSpecWatch-II is O(T^{2/3}), where T is the time horizon. Whereas, the actualweak regret of SpecWatch-III is O(T^{2/3}) with probability 1 - {\delta}, forany {\delta} in (0, 1). Both algorithms guarantee the upper bounds matching thelower bound of the general adversarial MAB- SC problem. Therefore, they are allasymptotically optimal.

SpecWatch: A Framework for Adversarial Spectrum Monitoring with Unknown Statistics