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User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality

Conghao Zhou, Jie Gao, Shisheng Hu, Nan Cheng, Weihua Zhuang, Xuemin Shen

TL;DR

This work tackles the challenge of timely frame uploading for edge-assisted MAR in 6G by introducing MARLIN, a per-user digital twin that captures user-specific MAR service demands through a customized data model and two DT operation functions that adaptively switch between detailed graph-based and simplified sequence-based traffic models. A Bayes-informed, robust resource-management approach is developed for single- and multi-user scenarios, with closed-form solutions guided by Theorems 1 and 2 to allocate radio spectrum while meeting a specified reliability $\epsilon$. Trace-driven simulations demonstrate that MARLIN more accurately models non-stationary uplink traffic, reduces resource over-provisioning compared to slicing-based approaches, and improves the likelihood of timely camera frame uploads by up to 14.2%. The approach highlights the practical potential of digital twin–driven, user-centric networking for 6G edge-enabled MAR and points to promising future work in hybridizing user-centric and slicing strategies.

Abstract

Future 6G networks are envisioned to facilitate edge-assisted mobile augmented reality (MAR) via strengthening the collaboration between MAR devices and edge servers. In order to provide immersive user experiences, MAR devices must timely upload camera frames to an edge server for simultaneous localization and mapping (SLAM)-based device pose tracking. In this paper, to cope with user-specific and non-stationary uplink data traffic, we develop a digital twin (DT)-based approach for user-centric communication service provision for MAR. Specifically, to establish DTs for individual MAR devices, we first construct a data model customized for MAR that captures the intricate impact of the SLAM-based frame uploading mechanism on the user-specific data traffic pattern. We then define two DT operation functions that cooperatively enable adaptive switching between different data-driven models for capturing non-stationary data traffic. Leveraging the user-oriented data management introduced by DTs, we propose an algorithm for network resource management that ensures the timeliness of frame uploading and the robustness against inherent inaccuracies in data traffic modeling for individual MAR devices. Trace-driven simulation results demonstrate that the user-centric communication service provision achieves a 14.2% increase in meeting the camera frame uploading delay requirement in comparison with the slicing-based communication service provision widely used for 5G.

User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality

TL;DR

This work tackles the challenge of timely frame uploading for edge-assisted MAR in 6G by introducing MARLIN, a per-user digital twin that captures user-specific MAR service demands through a customized data model and two DT operation functions that adaptively switch between detailed graph-based and simplified sequence-based traffic models. A Bayes-informed, robust resource-management approach is developed for single- and multi-user scenarios, with closed-form solutions guided by Theorems 1 and 2 to allocate radio spectrum while meeting a specified reliability . Trace-driven simulations demonstrate that MARLIN more accurately models non-stationary uplink traffic, reduces resource over-provisioning compared to slicing-based approaches, and improves the likelihood of timely camera frame uploads by up to 14.2%. The approach highlights the practical potential of digital twin–driven, user-centric networking for 6G edge-enabled MAR and points to promising future work in hybridizing user-centric and slicing strategies.

Abstract

Future 6G networks are envisioned to facilitate edge-assisted mobile augmented reality (MAR) via strengthening the collaboration between MAR devices and edge servers. In order to provide immersive user experiences, MAR devices must timely upload camera frames to an edge server for simultaneous localization and mapping (SLAM)-based device pose tracking. In this paper, to cope with user-specific and non-stationary uplink data traffic, we develop a digital twin (DT)-based approach for user-centric communication service provision for MAR. Specifically, to establish DTs for individual MAR devices, we first construct a data model customized for MAR that captures the intricate impact of the SLAM-based frame uploading mechanism on the user-specific data traffic pattern. We then define two DT operation functions that cooperatively enable adaptive switching between different data-driven models for capturing non-stationary data traffic. Leveraging the user-oriented data management introduced by DTs, we propose an algorithm for network resource management that ensures the timeliness of frame uploading and the robustness against inherent inaccuracies in data traffic modeling for individual MAR devices. Trace-driven simulation results demonstrate that the user-centric communication service provision achieves a 14.2% increase in meeting the camera frame uploading delay requirement in comparison with the slicing-based communication service provision widely used for 5G.

Paper Structure

This paper contains 28 sections, 2 theorems, 37 equations, 10 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Device Pose Tracking in MAR
  4. Communication Service Provision
  5. Edge-assisted Device Pose Tracking in MAR
  6. Considered Scenario
  7. Key Frame Uploading
  8. 3D Map Update & Synchronization
  9. Problem Formulation
  10. MAR User Digital Twin Establishment
  11. Data-driven Demand Modeling Function (DMF)
  12. Detailed Modeling
  13. D-State Transition Modeling
  14. Simplified Modeling
  15. Model Switching Function (MSF)
  16. ...and 13 more sections

Key Result

Theorem 1

The probability $P(\sum_{f \in \mathcal{F}_{t}}{a_{f}} \leq k_{t} | \hat{\mathbf{a}}_{t} )$ can be derived in eq30, which is a non-decreasing function given parameters $p_{t}$, $q_{t}$, and $\lambda_{t}$, where $\hat{A}_{t} = \sum_{f \in \mathcal{F}_{t}}{\hat{a}_{f}}$, $F_{t} = |\mathcal{F}_{t}|$, and

Figures (10)

  • Figure 1: An illustration of edge-assisted device pose tracking for one MAR device.
  • Figure 2: The workflow of the proposed DT-based user-centric communication service provision approach.
  • Figure 3: The hierarchical data model in the MUP.
  • Figure 4: Performance Comparison between MARLIN and Poisson model-based approach.
  • Figure 5: Performance of model switching in adapting to data traffic variations.
  • ...and 5 more figures

Theorems & Definitions (6)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • proof
  • proof