Emerging delay-critical edge AI applications, such as VR perception and real-time video analytics, impose stringent latency and reliability requirements on 5G networks. However, existing mobility management mechanisms are largely reactive and fail to adapt to dynamic network conditions, resulting in suboptimal handover decisions and degraded performance. In this paper, we present TARMM, a 5G Open Radio Access Network (O-RAN) system that optimizes user mobility management for delay-critical edge AI offloading. The core of TARMM is a temporal graph model that captures the spatiotemporal dynamics of the RAN across users and cells, enabling near real-time handover decisions. Building on this representation, we design a multi-agent reinforcement learning (MARL) framework with rule-based action masking and proactive resource preparation to ensure safe, stable, and efficient handovers. We implement TARMM on a multi-cell indoor 5G O-RAN testbed and evaluate it using diverse VR workloads. Extensive experiments show that TARMM reduces tail latency by up to 44% and packet loss by up to 56% compared to state-of-the-art approaches.

翻译：新兴的时延关键型边缘AI应用（如VR感知和实时视频分析）对5G网络提出了严格的低时延和高可靠性要求。然而，现有移动管理机制大多响应滞后，无法适应动态网络条件，导致切换决策欠佳及性能退化。本文提出TARMM——一种5G开放无线接入网（O-RAN）系统，用于优化时延关键型边缘AI卸载中的用户移动性管理。TARMM的核心是一个时序图模型，该模型捕捉了RAN中跨用户与小区间的时空动态特性，从而支持近实时切换决策。基于这一表示，我们设计了一个多智能体强化学习（MARL）框架，结合基于规则的行动掩码与主动资源准备机制，以确保切换的安全性、稳定性与高效性。我们在一个多小区室内5G O-RAN测试平台上实现了TARMM，并使用多样化的VR工作负载进行了评估。大量实验表明，与当前最先进方法相比，TARMM将尾部时延降低高达44%，丢包率降低高达56%。