A Q-Learning Based Routing Algorithm for Software Defined Vehicular Network

The traditional Vehicular Ad-hoc Network (VANET) routing algorithms are just designed based on the scenario of a single service traffic type and RAT, without designing personalized routing schemes for different types of service traffic. Software Defined Vehicular Network (SDVN) paradigm can solve these problems with its centralized control feature. We propose an intelligent SDVN architecture for the VANET scenarios with different types of service traffic and various RATs. Then we propose a hierarchical intelligent routing algorithm based on Q-learning considering the different personalized needs of various traffic types. The simulation results show that the algorithm can meet the personalized requirements of various traffic, and has better performance in delay-packet loss rate-cost.