Abstract: Considering the problem of complex topology in the rapid movement of vehicles in the network of vehicles, a dynamic network slicing resource deployment algorithm based on deep reinforcement learning is proposed. In the communication scenario of vehicle to infrastructure, for the changing vehicle topology and business requests, the slicing resource deployment problem is modeling as an observed Markov decision model, and the joint controller is used to monitor the network status in real time. The parameters are updated in real time according to the value of the actions in the distribution ratio of slicing resources, and a prioritized experience replay strategy is introduced to accelerate convergence speed, providing sufficient communication resources for each service request to interact with vehicle speed and location information. Simulation experiment results indicate that, compared to other algorithms, the proposed algorithm demonstrates better performance in end-to-end throughput, end-to-end latency, slice packet loss rate, and vehicle service request acceptance rate.

Key words: network slicing ,  vehicle to infrastructure communication ,  deep reinforcement learning ,  Markov decision process