Decentralized User Allocation and Dynamic Service for Multi-UAV-Enabled MEC System

With the promising techniques and mobile applications of the Internet of Things (IoT) in the fifth-generation (5G) wireless networks, the mobile edge computing (MEC) paradigm has been widely utilized to support the massive data processing and meet the computation demand of mobile devices. However, the service coverage is limited by the static servers. Therefore, the unmanned aerial vehicles (UAVs) with high mobility are deployed as aerial base stations (BS) to assist ground users to offload the task and process the data on edge. In this article, we introduce a decentralized user allocation and dynamic service scheme to assign the UAV deployment for a multi-UAV MEC system, which incorporates the deep reinforcement learning (DRL) technology into a two-layer training framework. The continuous action, including the UAV trajectory and bit allocation of offloading, is first optimized in the lower layer, while the upper one concentrates on the discrete action space for the user allocation. In particular, we develop a constraint reward function that considers the communication quality in the lower layer to accomplish efficient communication. The well-trained neural network is embedded in the upper layer to provide rapid feedback for the upper environment. Experimental results demonstrate that the proposed approach achieves superior performance than the baseline methods in terms of the user energy consumption.