Dynamic Load-Balancing Spectrum Decision for Heterogeneous Services Provisioning in Multi-Channel Cognitive Radio Networks

In this paper, we study dynamic load-balancing spectrum decision for a cognitive radio network (CRN) that dynamically distributes packets from the secondary user (SU) to different available primary channels. We consider two different classes of services at the SU, i.e., delay sensitive (DS) and best effort (BE) services, and assign a higher priority to the DS services. We apply priority queuing model to address this priority issue in the CRN. Based on the queuing model, two Markov decision processes (MDPs) are formulated with objectives to minimize the average delay of both services while guaranteeing the priority of the DS services. Reinforcement learning is applied to find the optimal solutions when the traffic and channel characteristics are unknown. To address the computational complexity issue in the MDP solutions, we propose a myopic method based on the estimated packet sojourn time, which is derived by formulating a phase type distribution. Simulation results demonstrate the effectiveness of all proposed algorithms for load-balancing spectrum decision. It also shows that the proposed myopic scheme can achieve significant reduction on computational complexity with a cost on the delay performance of low priority BE services.