Power Allocation of Energy Harvesting Cognitive Radio Based on Deep Reinforcement Learning
Huan Xie, Ruiquan Lin, Jun Wang, Min Zhang, Changchun Cheng
Abstract
In this paper, an underlay cognitive radio network with energy harvesting is considered which works in slotted mode. SU decides to transmit data or harvest energy from environment according to the available energy of the battery and the channel state in each slot. Considering the highly dynamic characteristics of channel occupancy, channel gain and energy arrival, a deep Q-network (DQN) algorithm in deep reinforcement learning is proposed. By setting the corresponding reward function under different channel occupancy states, the agent can select the appropriate working mode and value of transmit power according to the change of cognitive radio environment. After a period of learning, agent obtains the optimal strategy in finite time slots. Simulation results show that the proposed method can converge and perform better than other baseline strategies.