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

Conghao Zhou, Jie Gao, Yixiang Liu, Shisheng Hu, Nan Cheng, Xuemin Shen

TL;DR

This paper develops a digital twin (DT)based data-driven approach to 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.

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.

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

TL;DR

This paper develops a digital twin (DT)based data-driven approach to 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.

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.
Paper Structure (18 sections, 1 theorem, 12 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 18 sections, 1 theorem, 12 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Considered Scenario
  4. Key Frame Uploading
  5. 3D Map Update & Synchronization
  6. Problem Formulation
  7. The Developed Digital Twin-based Approach
  8. Data-driven Demand Modeling Function (DMF)
  9. Detailed Modeling
  10. State Transition Modeling
  11. Simplified Modeling
  12. Model Switching Function (MSF)
  13. MAR User Profile (MUP)
  14. M-UDT-based User-centric Service Provision
  15. Performance Evaluation
  16. ...and 3 more sections

Key Result

Theorem 1

The probability $P(N_{t} \ge \sum_{f \in \mathcal{F}_{t}}{a_{t,f}} | \hat{\mathbf{a}}_{t} )$ given prediction results from the M-UDT, can be derived in eq30, which is non-decreasing, where $\hat{A} = \sum_{f \in \mathcal{F}_{t}}{\hat{a}_{t,f}}$, $F = |\mathcal{F}_{t}|$, and

Figures (5)

  • Figure 1: The considered scenario of edge-assisted MAR.
  • Figure 2: The hierarchical data model in the MUP.
  • Figure 3: The workflow of the developed DT-based approach.
  • Figure 4: Data traffic modeling performance comparison.
  • Figure 5: Service provision performance of the M-UDT-based approach.

Theorems & Definitions (2)

  • Theorem 1
  • proof