Task Offloading and Resource Allocation in C-V2X based Air-Ground Integrated Vehicular Edge Computing Network

In contrast to the traditional vehicular edge computing (VEC) network, the air-ground integrated vehicular edge computing (AGI-VEC) network presents significant benefits characterized by continuous coverage, lower delay, and higher data rates for the Internet of vehicles (IoV). In this paper, considering the vehicles can be connected to the roadside units (RSUs) and unmanned aerial vehicles (UAVs) by using the cellular vehicle-toeverything (C-V2X) links, we investigate a joint task offloading and resource allocation problem in the AGI-VEC network with dual Uu/PC5 interface to minimize the task offloading delay. Due to the non-convexity of the problem, it is difficult to solve by utilizing the traditional methods. We transform it into a Markov decision process (MDP), and then propose a joint task offloading, power allocation, computation resource allocation, and UAV trajectory design (JTPCU) algorithm based on the multi-agent soft actor-critic (MASAC) method. Simulation results demonstrate that, compared to the other benchmark algorithms, the proposed algorithm achieves better performance in reducing task offloading delay.