Dynamic spectrum access and sharing through actor-critic deep reinforcement learning

Abstract

When primary users of the spectrum use frequency channels intermittently, secondary users can selectively transmit without interfering with the primary users. The secondary users adjust the transmission power allocation on the frequency channels to maximize their information rate while reducing channel conflicts with the primary users. In this paper, the secondary users do not know the spectrum usage by the primary users or the channel gains of the secondary users. Based on the conflict warnings from the primary users and the signal-to-interference-plus-noise ratio measurement at the receiver, the secondary users adapt and improve spectrum utilization through deep reinforcement learning. The secondary users adopt the actor-critic deep deterministic policy gradient algorithm to overcome the challenges of large state space and large action space in reinforcement learning with continuous-valued actions. In addition, multiple secondary users implement multi-agent deep reinforcement learning under certain coordination. Numerical results show that the secondary users can successfully adapt to the spectrum environment and learn effective transmission policies.