Resource Allocation Policy of Primary Users in Proactively-Optimization Cognitive Radio Networks

Cognitive radio (CR) as one of the most promising technologies to solve the problem of exhausting of spectrum resource, draws a lot of attention recently. However, for the highly intelligent wireless devices in the future, the assumption "CR devices introduce no interference to the primary users (PUs)" in traditional CR systems may do not exist anymore, due to that the devices are always trying to maximize their own reward, without considering the interference to the others. In this paper, the concept of proactively-optimization CR network is introduced to deal with this problem by enforcing ``punishment'' or ``penalty'' to illegal CR transmissions. Under the framework of proactively-optimization CR networks, with the reward function defined in this paper, the spectrum resource allocation decision making problem of primary users is modeled as a Markov decision process (MDP), and an optimal resource allocation scheme with the objective of maximizing the PU's reward is proposed. Furthermore, extensive simulation results show that the proposed scheme improves the reward significantly compared to the existing scheme.