A Spectrum Handoff Method Based on Reinforcement and Transfer Learning

Abstract

This paper designs a spectrum handoff method based on reinforcement and transfer learning in a cognitive radio environment. In the context of secondary users adopting reinforcement learning to form a spectrum handoff strategy, transfer learning is used to increase the convergence speed of reinforcement learning for new users. First, the original secondary user completes reinforcement learning in a radio environment. Then, the original secondary users are considered as an expert user, and the Q table obtained through reinforcement learning is transferred to the newly arrived secondary users. Finally, the new users complete their own reinforcement learning based on the Q table. Through simulation experiments, comparing the reinforcement learning convergence process of new secondary users with and without transfer learning, it is found that transfer learning can significantly improve the convergence rate of new users.