Stochastic Game Formulation for Cognitive Radio Networks

In this paper, we model the various wireless users in a cognitive radio network as a collection of selfish, autonomous agents that strategically interact in order to compete for the dynamically available spectrum opportunities. We propose a stochastic game framework to model how the competition among users for spectrum opportunities evolves over time. At each stage of the dynamic resource allocation, a spectrum moderator auctions the available resources and the users strategically bid for the required resources. Based on the observed resource allocation and corresponding rewards from previous allocations, we propose a best response learning algorithm that can be deployed by wireless users to improve their bidding policy at each stage. The simulation results show that by deploying the proposed best response learning algorithm, the wireless users can significantly improve their own performance in terms of both the packet loss rate and the incurred cost for the used resources.