Throughput Maximization of Hybrid Access in Multi-Class Cognitive Radio Networks With Energy Harvesting

In this paper, the hybrid interweave/underlay channel access mode is studied for an energy harvesting (EH) cognitive radio network with multi-class secondary users (SUs). The hybrid channel access mode combines the benefits of interweave transmission (opportunistic access with high throughput) and that of the underlay transmission (any time transmission with controlled power). EH upgrades the SUs' devices to be self sustainable. Additionally, classifying the SUs helps to meet their different quality of service (QoS) requirements. The system is modelled as a mixed observable Markov decision process (MOMDP) to handle the uncertainty in the primary user (PU) activity and consider future rewards. The MOMDP model is solved to maximize the SUs' throughput using two algorithms, namely, the point-based value iteration and the heuristic search value iteration (HSVI). Moreover, skipping the schedule of some SUs is proved to increase the channel utilization. The HSVI is proved to be efficient and reduces the time complexity significantly. Compared to related work in literature, the proposed model is proved to be superior in terms of throughput, tunable to meet the different QoS requirements of SU classes, and can accommodate any number of SU classes. Finally, the effect of some system parameters on the proposed system performance is studied and some insights are derived about the structure of the optimal policy and the system parameters values.