Profit maximization of cognitive virtual network operator in a dynamic wireless network

In this paper, we study a cognitive virtual network operator's profit maximization problem in a dynamic network environment. We consider various network dynamics, including dynamic user demands, unstable sensing spectrum resources, dynamic spectrum prices, and time-varying channel conditions. We develop a low-complexity on-line control policy that determines pricing and resource scheduling without knowing the distribution of dynamic network parameters. We show that the proposed algorithm can achieve arbitrarily close to the optimal profit with a proper trade-off of the queuing delay.