Opportunistic Spectrum Access with Dynamic Users: Directional Graphical Game and Stochastic Learning

Abstract

This paper investigates the channel selection problem with dynamic users and the asymmetric interference relation in distributed opportunistic spectrum access systems. Since users transmitting data are based on their traffic demands, they dynamically compete for the channel occupation. Moreover, the heterogeneous interference range leads to asymmetric interference relation. The dynamic users and asymmetric interference relation bring about new challenges such as dynamic random systems and poor fairness. In this article, we will focus on maximizing the tradeoff between the achievable utility and access cost of each user, formulate the channel selection problem as a directional graphical game and prove it as an exact potential game presenting at least one pure Nash equilibrium point. We show that the best NE point maximizes both the personal and system utility, and employ the stochastic learning approach algorithm for achieving the best NE point. Simulation results show that the algorithm converges, presents near-optimal performance and good fairness, and the directional graphical model improves the systems throughput performance in different asymmetric level systems.