Evolutionary Game and Learning for Dynamic Spectrum Access
Efficient dynamic spectrum access mechanism is crucial for improving the spectrum utilization. In this paper, the authors consider the dynamic spectrum access mechanism design with both complete and incomplete network information. When the network information is available, they propose an evolutionary spectrum access mechanism. They use the replicator dynamics to study the dynamics of channel selections, and show that the mechanism achieves an equilibrium that is an evolutionarily stable strategy and is also max-min fair. With incomplete network information, they propose a distributed reinforcement learning mechanism for dynamic spectrum access.