Robust Optimal Spectrum Patrolling for Passive Monitoring in Cognitive Radio Networks

Passive spectrum monitoring is important for network diagnosis and radio frequency management in spectrum-sharing wireless networks, i.e., cognitive radio network. Most of the related work focused on the sniffer-channel assignment problem, i.e, assigning proper operational channel to wireless sniffers with the aim of tracking and capturing the target signals or data packets. These approaches were usually designed for the scenarios in which the malicious or suspect wireless users are known. In this paper, we focus on the problem of spectrum patrolling, in which the sniffers have no specific targets, but try to patrol the interested temporal, spatial or spectrum areas. Once the periodicity or regularity of the wireless traffics is identified, a patrol path will be developed for routine patrolling. The path planning problem is formulated as a robust reward maximization problem with uncertain channel information. We propose an algorithm to determine the optimal solution and validate it through numerical simulations. Simulation results show that our proposed algorithm can achieve the maximal reward even with unknown information of the user activities.