Computation Offloading and Resource Allocation in Unmanned Aerial Vehicle Networks

Deploying unmanned aerial vehicles (UAVs) as mobile edge computing (MEC) servers has been attracting significant attention, since the UAVs' inherent maneuverability and mobility can further reduce the distance between the users and computational functionalities. In the UAV-assisted MEC systems, the caching and offloading decision optimization, subject to the computation and energy constraints of the UAVs, is one of the key design issues. Against this backdrop, in this paper, we propose a novel cloud-edge framework to facilitate MEC in the UAV networks. Specifically, in our framework, the edge UAVs (EUAVs), together with the cloud, provide caching and computing services for the terrestrial users. In order to minimize the weighted sum cost of latency and energy consumption, we jointly optimize the caching and offloading decisions, the EUAV deployment, the radio and computation resource allocation, while simultaneously satisfying the UAVs' cache and computation capacity constraints, as well as the users' latency and energy consumption constraints. To solve this NP-hard problem, we propose a sequential convex programming (SCP) and sequential quadratic programming (SQP) based deep Q-learning (SS-DQN) algorithm. The proposed algorithm allows the system to adaptively adjust caching and offloading decisions with the EUAV deployment scheme and the resource allocation scheme obtained by SCP and SQP algorithms, respectively. Simulation results verify the superiority of our proposed algorithm compared to two benchmark schemes, and demonstrate how the combination of DRL and convex optimization can improve the system performance significantly.