Self-Learning Repeated Game Framework for Distributed Primary-Prioritized Dynamic Spectrum Access
Dynamic spectrum access has become a promising approach to fully utilize the scarce spectrum resources. In a dynamically changing spectrum environment, it is very important to design a distributed access scheme that can coordinate different users' access adapt to spectrum dynamics with only local information. In this paper, we propose a self-learning repeated game framework for distributed primary-prioritized dynamic spectrum access through modeling the interactions between secondary users as a noncooperative game. With the proposed framework, the inefficiency due to users' selfish behavior can be highly improved, and the secondary users can distributively obtain their optimal access probabilities with only local observations. The simulation results show that the proposed framework can achieve comparable performances with those of the centralized primary-prioritized dynamic spectrum access scheme.