An Improved Double Deep Q Network for Multi-user Dynamic Spectrum Access

With the rapid development of mobile communication technology, the contradiction between the limited spectrum utilization resources and the demand of a lot of spectrum communication is increasingly aggravated. New intelligent methods are needed to improve the utilization rate of spectrum. A multi-user dynamic spectrum access method based on distributed priority experience pool and double deep Q network is proposed. This method can help the secondary users to continuously learn according to their perceived environment information in the dynamic environment, and choose the idle channel to complete the spectrum access task for improving the spectrum utilization rate. In this method, a distributed reinforcement learning framework is adopted, and each secondary user is regarded as an agent. Each agent learns by using standard single-agent reinforcement learning method to reduce the underlying computing overhead. In addition, the method adds priority sampling on the basis of neural network training, and then optimizes the training efficiency of neural network to help sub-users choose the optimal strategy. The simulation results show that this method can improve the success rate, reduce the collision rate and improve the communication rate.