Channel Selection in Multi-channel Multi-user RF Energy Harvesting Cognitive Radio Networks

Recently in wireless networks, spectrum scarcity and energy consumption are becoming important issues. Radio Frequency Energy Harvesting Cognitive Radio (RF-EH-CR) networks play a key role to solve this problem. Channel selection is an important aspect which affects the throughput of the CR network. In this paper, we compare the performance of DRQoSUCB and DRCA reinforcement learning algorithms for the network of energy harvesting cognitive radio nodes. The secondary nodes are non-cooperative and are not aware of each other's state. The EH-RCA policy is seen to be better than EH-RQUCB policy as number of user increases.