Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

Abstract

Considering the user mobility and unpredictable mobile edge computing (MEC) environments, this paper studies the intelligent task offloading problem in unmanned aerial vehicle (UAV)-enabled MEC with the assistance of digital twin (DT). We aim at minimizing the energy consumption of the entire MEC system by jointly optimizing mobile terminal users (MTUs) association, UAV trajectory, transmission power distribution and computation capacity allocation while respecting the constraints of mission maximum processing delays. Specifically, double deep Q-network (DDQN) algorithm stemming from deep reinforcement learning is first proposed to effectively solve the problem of MTUs association and UAV trajectory. Then, the closed-form expression is employed to handle the problem of transmission power distribution and the computation capacity allocation problem is further addressed via an iterative algorithm. Numerical results show that our proposed scheme is able to converge and significantly reduce the total energy consumption of the MEC system compared to the benchmark schemes.

arXiv comment14 pages, 11 figures

References in corpus (1)

• Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks

Cited by in corpus (4)

• Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation
• FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing
• Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model
• Delay-Aware Digital Twin Synchronization in Mobile Edge Networks with Semantic Communications