Admission Control Using Stochastic Learning Games in Cognitive Radio Networks

Abstract

A cognitive radio wireless network is an emerging communication paradigm to effectively address spectrum scarcity challenge. In this paper, we investigate the performance improvement gained by applying cognitive radio to a multiple Wireless Service Providers (WSPs). We consider several independent WSPs and two types of users: primary (licensed) and secondary (unlicensed) users. We have proposed a scheme for secondary users to manage their handoff based on a threshold on the number of channels fixed by the WSP. As each WSP evolves in a random environment, we propose a game theoretic framework to find a Nash equilibrium and a stochastic learning algorithm to converge to this equilibrium. A Markov process, with continuous time and finite state space, models the system. Numerical results show the system equilibrium points and the conditions to converge to the best one of them to increase the spectrum utilization of cognitive users.