Research on 5G Heterogeneous Ultra Dense network selection algorithm based on Dueling- DDQN

Abstract

Abstract In the 5G Heterogeneous Ultra Dense Network environment, due to the dense deployment of the network and the diversity of user service types, a more flexible network selection algorithm is required to reduce the network blocking rate and improve the user’s quality of service (QoS). Considering the QoS requirements and preferences of the users, a network selection algorithm based on Dueling-DDQN is proposed by using deep reinforcement learning, which defines the state, action space and reward function of the different services to maximize the cumulative reward value of the network selection algorithm. The simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the network blocking rate while reducing the switching times.