Abstract-We consider the problem of spectrum sharing in a cognitive radio system consisting of a primary user and a secondary user. The primary user and the secondary user work in a non-cooperative manner. Specifically, the primary user is assumed to update its transmit power based on a pre-defined power control policy. The secondary user does not have any knowledge about the primary user's transmit power, or its power control strategy. The objective of this paper is to develop a learning-based power control method for the secondary user in order to share the common spectrum with the primary user. To assist the secondary user, a set of sensor nodes are spatially deployed to collect the received signal strength information at different locations in the wireless environment. We develop a deep reinforcement learning-based method, which the secondary user can use to intelligently adjust its transmit power such that after a few rounds of interaction with the primary user, both users can transmit their own data successfully with required qualities of service. Our experimental results show that the secondary user can interact with the primary user efficiently to reach a goal state (defined as a state in which both users can successfully transmit their data) from any initial states within a few number of steps.
REF adopted DRL to learn the power adaption strategy of the primary user in a cognitive network, such that the secondary user is able to adaptively control its power and satisfy the required quality of services of both primary and secondary users.
Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach
Computer Science Mathematics