Recent works have shown that the white-space spectrum opened to cognitive radio devices is far less than what the lobbyists claimed. With fast growing number of secondary users, carefully scheduling the spectrum allocation in cognitive radio networks operating on white space becomes vital. However, the frequent ON/OFF activity of primary users (PU) and the mobility of the cognitive users make the problem of spectrum scheduling extremely hard. By modeling the PUs activity in an opportunistic manner, this paper studies how to schedule the spectrum assignment for mobile cognitive radio devices. With the mobility information, we formally define the related problem as the Maximum Throughput Channel Scheduling problem (MTCS) which seeks a channel assignment schedule for each cognitive radio device such that the maximum expected throughput can be achieved. We present a general scheduling framework for solving the MTCS. Based on the proposed framework, we then present two polynomial time optimal algorithms to solve the MTCS in the homogeneous and the heterogeneous traffic load cases, respectively. Our algorithms are evaluated by simulations using the mobility trace obtained from a real world public transportation system. On average, the proposed algorithms outperform a greedy algorithm by 21.6%.
