1 research outputs found
SpecWatch: A Framework for Adversarial Spectrum Monitoring with Unknown Statistics
In cognitive radio networks (CRNs), dynamic spectrum access has been proposed
to improve the spectrum utilization, but it also generates spectrum misuse
problems. One common solution to these problems is to deploy monitors to detect
misbehaviors on certain channel. However, in multi-channel CRNs, it is very
costly 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 determine
how long to monitor each channel and in which order to monitor, because
switching channels incurs costs. Moreover, the information about the misuse
behavior is not available a priori. To answer those questions, we model the
spectrum monitoring problem as an adversarial multi-armed bandit problem with
switching costs (MAB-SC), propose an effective framework, and design two online
algorithms, SpecWatch-II and SpecWatch-III, based on the same framework. To
evaluate the algorithms, we use weak regret, i.e., the performance difference
between the solution of our algorithm and optimal (fixed) solution in
hindsight, as the metric. We prove that the expected weak regret of
SpecWatch-II is O(T^{2/3}), where T is the time horizon. Whereas, the actual
weak regret of SpecWatch-III is O(T^{2/3}) with probability 1 - {\delta}, for
any {\delta} in (0, 1). Both algorithms guarantee the upper bounds matching the
lower bound of the general adversarial MAB- SC problem. Therefore, they are all
asymptotically optimal