Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

With the development of wireless communication technology, requirement for data rate is growing rapidly. Mobile system faces problem shortage spectrum resources. Cognitive radio technology allows secondary users to use frequencies authorized primary user with permission user, which can effectively improve utilization In this article, we establish a cognitive network model based on underlay and propose resource allocation algorithm DDQN (Double Deep Q Network). The jointly optimizes efficiency QoE (Quality Experience) through channel selection power control users. Simulation results show that proposed spectral QoE. Compared Q-learning DQN, converge faster obtain higher shows more stable efficient performance.