Dynamic spectrum access based on prior knowledge enabled reinforcement learning with double actions in complex electromagnetic environment

The spectrum access problem of cognitive users in the fast-changing dynamic interference spectrum environment is addressed in this paper. The prior knowledge for the dynamic spectrum access is modeled and a reliability quantification scheme is presented to guide the use of the prior knowledge in the learning process. Furthermore, a spectrum access scheme based on the prior knowledge enabled RL (PKRL) is designed, which effectively improved the learning efficiency and provided a solution for users to better adapt to the fast-changing and high-density electromagnetic environment. Compared with the existing methods, the proposed algorithm can adjust the access channel online according to historical information and improve the efficiency of the algorithm to obtain the optimal access policy. Simulation results show that, the convergence speed of the learning is improved by about 66% with the invariant average throughput.