Distributed Opportunistic Spectrum Access in an Unknown and Dynamic Environment: A Stochastic Learning Approach

摘要

In this paper, the problem of distributed throughput maximization in an opportunistic spectrum access network with multiple secondary users (SUs) and multiple primary channels is investigated. To address the challenges in designing efficient solutions in a dynamic and unknown environment, we formulate the optimization problem as a noncooperative game, which is further proved to be an ordinal potential game. We then propose a best-response-based algorithm to achieve the Nash equilibrium points (NEPs) of the formulated game, given that there exists a coordinator for SUs to work in a round-robin fashion and a common control channel for SUs to exchange their information. To further relieve the system overhead due to information exchange among SUs, we design a new stochastic learning automata (SLA)-based algorithm, called N-SLA, which can converge to the pure-strategy NEPs of the formulated ordinal potential game in a fully distributed way. To our best knowledge, we are the first to address the convergence issue of the SLA-based algorithms for general ordinal potential games. Simulation results validate the effectiveness of our proposed algorithms.