Anchored Network Users: Stochastic Evolutionary Dynamics of Cognitive Radio Network Selection

Abstract

To solve the spectrum scarcity problem, the cognitive radio technology involves licensed users and unlicensed users. A fundamental issue for the network users is whether it is better to act as a licensed user by using a primary network or an unlicensed user by using a secondary network. To model the network selection process by the users, the deterministic replicator dynamics is often used, but in a less practical way that it requires each user to know global information on the network state for reaching a Nash equilibrium. This paper addresses the network selection process in a more practical way such that only noise-prone estimation of local information is required and, yet, it obtains an efficient system performance.