Adaptive Task Offloading in High-Speed Vehicular Networks Using Enhanced D3QN Algorithm

With the development of Vehicle Edge Computing (VEC), vehicles can offload tasks to Road Side Units (RSU) for processing. However, optimizing latency is a challenging problem in high-speed environments. This paper investigates the latency optimization problem for task offloading in high-speed environments, where multiple vehicles on the user end can choose to offload their tasks to either RSUs or Base Station (BS) for computation. Meanwhile, based on the vehicle’s location and speed, we predict the time a vehicle spends within the communication range of an RSU to select a more suitable edge node. Then, an improved Group Priority Sampling-Double Dueling Deep Q Network (GPS-D3QN) algorithm is used to optimize the proposed task offloading, resulting in an optimal offloading strategy that reduces latency. Through simulation, by varying the number of vehicles and RSUs, the proposed optimization scheme is verified to have an advantage in terms of delay compared to other schemes.