Dynamic Resource Scheduling Based Quality of Service Optimisation in Multi-UAV-Assisted City Edge Network Systems

The paradigm of unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) has emerged as an effective scheme for handling intensive tasks in heterogeneous networks. In this work, we consider a user-equipment-rich city network scenario. Due to the limited user equipments (UEs) resources and base station (BS) coverage, we utilise multiple-UAV-assisted UEs and partial offloading to handle the tasks. Meanwhile, considering the impact of task diversity on the quality of service (QoS) of the system, we design an integrated scheme that combines improved clustering techniques and deep reinforcement learning (DRL) for dynamic resource scheduling. Firstly, a random forest-based clustering algorithm (RFCA) is used to cluster UEs according to the service requirements (SR) of tasks, as a way to reduce the complexity of task processing and user association. Then a DRL-based computational offloading and bandwidth allocation algorithm (DCOBA) is used to improve the QoS by jointly optimising UAV-user associations, offloading ratios, and bandwidth allocation to reduce the system latency and energy consumption. Finally, experimental simulation data shows that our scheme can better optimise the Qos compared to traditional schemes.