Sniffer Channel Assignment With Imperfect Monitoring for Cognitive Radio Networks
Jing Xu, Qingsi Wang, Kai Zeng, Mingyan Liu, Wei Liu · IEEE Transactions on Wireless Communications · 2015
Sniffer channel assignment (SCA) is a fundamental building block for wireless data capture, which is essential for traffic monitoring and network forensics. Most of the existing SCA approaches for cognitive radio networks (CRNs) adopt optimization-based methods and rely on the prior knowledge of the secondary user (SU) activities. To relax this constraint, learning-based methods have been recently developed; however, there is still insufficient theoretical understanding within the learning framework for SCA. In this paper, we aim to maximize the total amount of the captured SU traffic, and we formulate the SCA problem as a nonstochastic/adversarial multiarmed bandit problem. Moreover, the inherent error in wireless capturing, i.e., imperfect monitoring, is considered in our model. We propose two online learning algorithms for the SCA scenarios with and without channel switching costs, respectively, and their regret performances are proved uniformly sublinear in time and polynomial in the number of channels. The numerical evaluation shows, in addition to their robust regret performances, the proposed algorithms greatly outperform the existing SCA approaches in the amount of effectively captured SU traffic.