Digital-Twin-Assisted Task Offloading Based on Edge Collaboration in the Digital Twin Edge Network

Emerging digital twin (DT) and mobile-edge computing (MEC) are crucial for enabling the rapid development of 6G. However, the existing works ignore the edge collaboration, which can provide the system with additional performance gain. In this article, we study the problem of mobile users (MUs) intelligently offloading tasks to cooperative mobile-edge servers (MESs) with the assistance of DT. Specifically, a DT-assisted task offloading scheme (DTTOS) that consists of the selection of MESs and intelligent task offloading is proposed. Channel state information (CSI) and blockchain are employed to implement the selection of MESs. Then, we present a solution to enable MU’s task offloading that is modeled as a Markov decision process (MDP) in an intelligent way. After this, a mathematical optimization model aiming at decreasing power and time overhead is formulated. In view of the complexity, it is decomposed into two suboptimization models and solved by the decision tree algorithm (DTA) and double deep- $Q$ -learning (DDQN), respectively. Simulations are conducted to prove the superiority of the proposed scheme in terms of data security assurance and network performance improvement.

I. Introduction

An integrated network across regions, airspace, and sea areas will be constructed in 6G to realize global seamless coverage in a true sense [1]. From the perspective of network performance indicators, 6G will greatly improve in terms of the transmission rate, end-to-end delay, reliability, connection density, spectrum efficiency, and energy efficiency, so as to meet the diverse network needs of various vertical industries [2], [3].