用AI代理优化移动端AR的通信资源，提升体验同时降低带宽消耗。
QoE-Aware Service Provision for Mobile AR Rendering: An Agent-Driven Approach
- 用大模型驱动的数字代理连接AR服务与网络，解决信息不通问题。
- 基于用户感知建模，实现个性化资源分配，提升用户体验质量。
- 实测比传统方法更精准省带宽，适合6G时代AR应用部署。
移动增强现实（MAR）被视为6G的关键沉浸式应用，通过设备姿态估计实现虚拟内容与物理环境的同步渲染。本文提出一种面向边缘辅助MAR的代理驱动通信服务供给方法，在降低MAR设备与边缘服务器间通信开销的同时保障用户体验质量（QoE）。首先，为解决网络控制器难以获取MAR应用特定信息的问题，我们构建由大语言模型（LLMs）驱动的数字代理，代表MAR服务提供商，弥合MAR服务与网络域之间的数据与功能鸿沟。其次，针对单个设备数据流量模式具有用户依赖性和动态性的问题，设计了一种用户级QoE建模方法，捕捉通信资源需求与用户感知体验之间的关系，实现个性化的代理驱动资源管理。基于实际流量的仿真结果表明，该方法在用户级QoE建模精度和通信资源效率方面均优于传统基于LLM的QoE感知服务供给方案。
原文摘要 · Abstract (English)
Mobile augmented reality (MAR) is envisioned as a key immersive application in 6G, enabling virtual content rendering aligned with the physical environment through device pose estimation. In this paper, we propose a novel agent-driven communication service provisioning approach for edge-assisted MAR, aiming to reduce communication overhead between MAR devices and the edge server while ensuring the quality of experience (QoE). First, to address the inaccessibility of MAR application-specific information to the network controller, we establish a digital agent powered by large language models (LLMs) on behalf of the MAR service provider, bridging the data and function gap between the MAR service and network domains. Second, to cope with the user-dependent and dynamic nature of data traffic patterns for individual devices, we develop a user-level QoE modeling method that captures the relationship between communication resource demands and perceived user QoE, enabling personalized, agent-driven communication resource management. Trace-driven simulation results demonstrate that the proposed approach outperforms conventional LLM-based QoE-aware service provisioning methods in both user-level QoE modeling accuracy and communication resource efficiency.
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