Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

TL;DR

This paper proposes a digital twin-assisted approach for UAV-enabled mobile edge computing that optimizes task offloading, UAV trajectory, and resource allocation to minimize energy consumption under delay constraints.

Contribution

It introduces a novel combination of digital twin technology and deep reinforcement learning for joint optimization in UAV-based MEC systems.

Findings

Significant reduction in energy consumption compared to benchmarks.

Effective convergence of the proposed optimization scheme.

Demonstrated benefits of digital twin assistance in dynamic MEC environments.

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.