RANPilot: Making AI Functionalities Robust to Dynamic O-RAN Reconfigurations

2026-07-06

AI Summary

This work addresses the severe performance degradation of AI models in O-RAN caused by abrupt shifts in data distribution during dynamic radio access network (RAN) reconfiguration, which conventional passive retraining strategies fail to mitigate without prolonged service disruption. To overcome this limitation, the authors propose RANPilot, a novel framework that introduces the first proactive AI adaptation mechanism tailored for O-RAN. RANPilot employs a lightweight, trajectory-driven virtual O-RAN simulator to generate high-fidelity synthetic data reflecting the target configuration prior to physical reconfiguration, thereby enabling preemptive model adaptation. This paradigm shift—from reactive retraining to anticipatory preparation—significantly enhances service continuity. Experimental validation on a real-world 5G testbed demonstrates an 85%–94% reduction in AI service interruption time, achieving near-seamless functional transitions.

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.

Problem

• O-RAN
• AI robustness
• data drift
• dynamic reconfiguration
• model degradation

Innovation

• proactive AI adaptation
• virtual O-RAN
• data drift mitigation
• trace-driven emulation
• O-RAN reconfiguration