Efficient Resource Allocation in Multi-UAV Assisted Vehicular Networks With Security Constraint and Attention Mechanism

With the rapid development of intelligent transportation systems, there is an increasingly strong demand for low-latency and high-bandwidth vehicular services. Unmanned aerial vehicles (UAVs) can be used as a supplement to the ground networks, to relieve the communication pressure on ground facilities, such as base stations. In this paper, we use multiple UAVs to provide services for vehicles and model the multi-UAV scenario as a collaborative multi-agent system. In addition, we take vehicle safety as the top priority and the delay requirement as the constraints. Then we exploit the Lagrange multiplier to combine the constraint function and cost function, so as to reduce the resource consumption as much as possible on the premise of ensuring the safety of the vehicles. The influence of spectrum efficiency and computing power should also be taken into account when allocating resources. We adopt the multi-agent reinforcement learning to train the UAVs, and meanwhile introduce the attention mechanism so that each UAV can optimize itself better with the information of other UAVs. Through extensive simulations, the effectiveness of our proposed method is verified. Particularly, the limited resources can allocated efficiently according to the vehicle's needs under the premise of ensuring vehicle safety.