PPO-based Computation Offloading for UAV-Assisted Mobile Edge Computing Networks
Ruibin Guo, Dong Yeol Yang, Yuming Zhang, Weiliang Chen, Mingyuan Liu, Zhan Liu, Hongke Zhang · 2024
Unmanned Aerial Vehicles (UAVs) provide a flexible working paradigm for device-cloud communication. Besides working as a relay between devices and clouds, UAVs can also provide mobile edge computing (MEC) services. In this paper, we investigate a computation offloading problem for UAV-assisted MEC networks in which local devices, UAVs, and clouds collaboratively process computing tasks to achieve energy-saving and latency reduction. In such green UAV-assisted MEC networks, we treat the same energy consumption of task processing differently due to processing location and assign different weights to the energy consumption of devices, UAVs, and clouds. Specifically, we propose a two-stage computation offloading framework including 1) the device clustering stage to determine the device cluster connected to certain UAVs and 2) the network operation stage to conduct computation offloading. We formulate the offloading process as a stochastic optimization problem to minimize the offloading cost. Furthermore, we decouple the optimization problem into a UAV selection subproblem and an offloading decision subproblem. Particularly, for the former subproblem, we employ a simulated annealing-based algorithm to minimize the total transmit energy of devices and UAVs. For the latter, we utilize a proximal policy optimization-based offloading algorithm to ascertain the processing locations of computing tasks. Simulation results show that the proposed algorithm outperforms in terms of energy reservation and latency reduction.