SpecWatch: A Framework for Adversarial Spectrum Monitoring with Unknown Statistics

by   Ming Li, et al.

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 - δ, for any δ 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.


page 1

page 2

page 3

page 4


Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints

We consider the classical stochastic multi-armed bandit problem with a c...

Online learning with feedback graphs and switching costs

We study online learning when partial feedback information is provided f...

Cost-Aware Learning and Optimization for Opportunistic Spectrum Access

In this paper, we investigate cost-aware joint learning and optimization...

Dynamic Spectrum Access using Stochastic Multi-User Bandits

A stochastic multi-user multi-armed bandit framework is used to develop ...

Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access

A stochastic multi-user multi-armed bandit framework is used to develop ...

The Value of Information and Efficient Switching in Channel Selection

We consider a collection of statistically identical two-state continuous...

Cleaning up the neighborhood: A full classification for adversarial partial monitoring

Partial monitoring is a generalization of the well-known multi-armed ban...

Please sign up or login with your details

Forgot password? Click here to reset