User-centric Service Provision for Edge-assisted Mobile AR: A Digital Twin-based Approach

TL;DR

This paper proposes a digital twin-based, data-driven approach to optimize spectrum resource reservation for user-centric service provision in edge-assisted mobile AR, addressing non-stationary traffic and complex uploading mechanisms.

Contribution

It introduces a hierarchical data model and an adaptive algorithm for traffic modeling, along with a closed-form solution for service provisioning in dynamic AR environments.

Findings

Outperforms conventional methods in adaptivity and robustness

Effectively models non-stationary data traffic

Enhances timely camera frame uploading in mobile AR

Abstract

Future 6G networks are envisioned to support mobile augmented reality (MAR) applications and provide customized immersive experiences for users via advanced service provision. In this paper, we investigate user-centric service provision for edge-assisted MAR to support the timely camera frame uploading of an MAR device by optimizing the spectrum resource reservation. To address the challenge of non-stationary data traffic due to uncertain user movement and the complex camera frame uploading mechanism, we develop a digital twin (DT)-based data-driven approach to user-centric service provision. Specifically, we first establish a hierarchical data model with well-defined data attributes to characterize the impact of the camera frame uploading mechanism on the user-specific data traffic. We then design an easy-to-use algorithm to adapt the data attributes used in traffic modeling to the non-stationary data traffic. We also derive a closed-form service provision solution tailored to data-driven traffic modeling with the consideration of potential modeling inaccuracies. Trace-driven simulation results demonstrate that our DT-based approach for user-centric service provision outperforms conventional approaches in terms of adaptivity and robustness.