Deep Reinforcement Learning for Dynamic Multichannel Access in Multi-Cognitive Radio Networks

Abstract

We propose a reinforcement learning framework based on deep recurrent learning to solve the dynamic spectrum access problem in the scenario where multiple cognitive networks coexist. In this scenario, the shared spectrum is divided into multiple channels, and the channel occupation by the primary user is modeled as a Markov model. The observation of the channel status by secondary users in this area obeys the partially observed Markov process, that is, each user can only observe the status of one channel in each time slot, and cannot obtain global information. To avoid collision in different cognitive networks, we adopt the distributed multi-agent deep reinforcement learning method. Without the interaction of different cognitive networks, based on the current partial observations, a suitable channel access policy can be obtained by a neural network. Comparing with Slotted-aloha and DQN-DSA Algorithm, the result indicates DRQN-DSA proposed in this paper performs better.