Equilibrium sensing time for distributed opportunistic access incognitive radio networks

In this paper, we consider a distributed opportunistic access (D-OSA), in which cognitive radio (CR) users attempt to access a channel licensed to a primary network. In this context, we formulate the problem of designing the equilibrium sensing time in a distributed manner, in order to maximize the throughput of CR users while guarantying a good protection to the primary users (PU). Next, we study the Nash equilibrium of the system, we also propose a combined learning algorithm for continuous actions that is fully distributed, and allows to the CR users to learn their equilibrium payoffs and their equilibrium sensing time. The simulation results show that the system can learn the sensing time and converge to a unique Nash equilibrium, which come to prove the theoretical study. A surprising feature is that there exists a correlation between the transmit probability and the sensing time. More precisely, lower transmit probability induces lower sensing times.