RANPilot: Making AI Functionalities Robust to Dynamic O-RAN Reconfigurations
2026-07-06 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors address a problem in Open Radio Access Networks (O-RAN) where AI models that help control the network stop working well after the network changes. Normally, retraining these AI models takes too long and causes network problems. They created RANPilot, which makes a virtual copy of the network to prepare and retrain AI models before actual changes happen. Their tests show this approach greatly reduces the downtime of AI services during updates. This helps keep O-RAN running smoothly with smarter, quicker AI adaptation.
Open Radio Access NetworkO-RANAI model retrainingdata drift5G testbednetwork reconfigurationvirtual emulatorproactive AI adaptationreactive retrainingdigital twin
Authors
Shimin Yu, Leming Shen, Jianing Zhang, Xin Li, Xianjin Xia, Yuanqing Zheng, Yaxiong Xie
Abstract
The Open Radio Access Network (O-RAN) promises unprecedented flexibility through its reconfigurable architecture and AI-driven control. However, this agility exposes a critical fragility: AI models trained on one network configuration suffer significant performance degradation after an upgrade due to dramatic data drift. The standard solution, reactive retraining, is unacceptably slow, leaving the network in a suboptimal state for tens of minutes and undermining the core benefits of O-RAN's dynamism. This paper introduces RANPilot, the first framework to address this challenge through proactive AI adaptation. RANPilot constructs a lightweight "virtual O-RAN" (a trace-driven emulator) to synthesize high-fidelity training data representing the post-reconfiguration state before the physical change occurs, allowing AI models to be adapted in advance. Extensive experiments on a real-world 5G testbed demonstrate that RANPilot achieves near interruption-free AI services upon reconfiguration, reducing AI downtime by 85% to 94% against reactive baselines. By shifting the AI evolution paradigm from reactive redevelopment to proactive preparation, RANPilot explores a digital-leadoff approach to enable robust AI in reconfigurable O-RAN deployments.