TARMM: Scaling Delay-Critical Edge AI Offloading in 5G O-RAN via Temporal Graph Mobility Management

TL;DR

TARMM is a system that uses a temporal graph and multi-agent reinforcement learning to optimize handovers in 5G O-RAN for delay-critical edge AI applications, significantly reducing latency and packet loss.

Contribution

It introduces a novel temporal graph model and MARL framework for proactive, stable mobility management in 5G O-RAN tailored for delay-sensitive edge AI workloads.

Findings

Reduces tail latency by up to 44%

Decreases packet loss by up to 56%

Demonstrates effectiveness on a multi-cell indoor 5G testbed

Abstract

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. Source code and demo videos are available at: https://margo-source.github.io/Margo/