Dynamic Spectrum Sharing Using Learning for Delay-Sensitive Applications

In this paper, we model the various wireless users streaming delay-sensitive data in a wireless network as a collection of selfish, autonomous agents that strategically interact in order to acquire the spectrum opportunities. The spectrum allocation is coordinated by a central spectrum moderator which deploys an incentive compatible mechanism. We further model the repeated spectrum competition as a stochastic game through which we are able to characterize the interaction among wireless users. 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 the accuracy of their private information 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.