Cooperative Task offloading and Dispatching Optimization for Large-scale Users via UAVs and HAP

With the development of the 6th generation communication technology, the service traffic of mobile communication is rapidly growing. Many new types of services usually have high requirements for computing resources and low latency constraints. They need to be offloaded to a base station (BS) with computing resources for processing. In some disaster areas, the communication system will go down due to damage to the ground infrastructure. High altitude platforms (HAPs) with extensive coverage and unmanned aerial vehicles (UAVs) with simple deployment can provide various emergency services as aerial BS. UAVs and HAP carry servers and other equipment to serve users. It is a promising technology for communication and computing services. Due to UAVs' limited computing resources and energy, it is a challenge to deploy them effectively and fully use network resources. Therefore, a task-dynamic processing through multi-UAV cooperation (TDPUC) strategy is proposed. A improved Kmeans algorithm is proposed to realize the dynamic deployment, which optimizes the number of UAVs dispatched and reduces the overall energy consumption. In addition, the multi-UAV cooperation for task offloading can realize dynamic task processing under constrained energy and resources. When UAVs cooperate, the multi-agent reinforcement learning (MARL) algorithm is used to optimize resource allocation and learns online. By numerical results, the proposed TDPUC strategy can improve the service capacity of tasks by 11% on average with less energy consumption.