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

arXiv: https://arxiv.org/abs/2509.25905 · HTML: https://mlsanity.com/paper/2509.25905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1402.1128/) (2014)
- [InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset](https://mlsanity.com/paper/1809.00716/) (2018)
- [Toward Immersive Communications in 6G](https://mlsanity.com/paper/2303.05283/) (2023)
- [Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G](https://mlsanity.com/paper/2402.01665/) (2024)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality](https://mlsanity.com/paper/2205.13770/) (2022)
- [Attention and Anticipation in Fast Visual-Inertial Navigation](https://mlsanity.com/paper/1610.03344/) (2016)
- [Digital Twin-based 3D Map Management for Edge-assisted Device Pose Tracking in Mobile AR](https://mlsanity.com/paper/2311.04997/) (2023)
- [AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization](https://mlsanity.com/paper/2301.04620/) (2023)
- [User-centric Immersive Communications in 6G: A Data-oriented Framework via Digital Twin](https://mlsanity.com/paper/2410.02688/) (2024)
- [Adaptive Device-Edge Collaboration on DNN Inference in AIoT: A Digital Twin-Assisted Approach](https://mlsanity.com/paper/2405.17664/) (2024)
- [User-centric Service Provision for Edge-assisted Mobile AR: A Digital Twin-based Approach](https://mlsanity.com/paper/2409.00324/) (2024)

## Built on by (most cited first, 1 of 1)

- [Enhancing Text-to-Image Generation via End-Edge Collaborative Hybrid Super-Resolution](https://mlsanity.com/paper/2601.14741/) (2026)

## Abstract (arXiv)

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.