Heterogeneous Network Selection Algorithm Based on Reinforcement Learning

In the current network environment, multiple wireless access technologies coexist. In order to meet the needs of various services in heterogeneous networks, in order to enable each user to select the most appropriate network to provide services and adapt to the dynamic changes of the network environment, this paper defines the markov decision-making process of network selection based on reinforcement learning, taking the heterogeneous network constructed by PDT and B-trunC as the background A network access control algorithm based on reinforcement learning is proposed, which fully considers the service type of session and the mobility of terminal, and realizes the adaptability of network selection.