Secondary User Monitoring in Unslotted Cognitive Radio Networks with Unknown Models

Cognitive radio networking (CRN) is a promising technology to improve the spectrum utilization by allowing secondary users (unlicensed users) to opportunistically access white space (spectrum holes) in licensed bands. Monitoring the detailed characteristics of an operational cognitive radio network is critical to many system administrative tasks. However, very limited work has been done in this area. In this paper, we study the passive secondary user monitoring problem in an unslotted cognitive radio network, where the users’ traffic statistics are unknown in priori. We formulate the problem as a multi-armed bandit (MAB) problem with weighted virtual reward. We propose a dynamic sniffer-channel assignment policy to capture as much as interested user data. Simulation results show that the proposed policy can achieve a logarithmic regret with relative scalability.

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