AI 中文总结
研究O-RAN中AI模型因配置升级性能下降问题，核心方法是构建‘虚拟O-RAN’合成训练数据提前适配，主要贡献是大幅减少AI停机时间，实现近无中断AI服务。
AI 中文摘要
开放无线接入网络（O-RAN）通过其可重构架构和人工智能驱动的控制带来前所未有的灵活性。然而，这种灵活性也暴露出一个关键的脆弱性：在一种网络配置上训练的人工智能模型在升级后会因数据剧烈漂移而性能大幅下降。标准的反应式重新训练解决方案速度过慢，使网络在次优状态下停留数十分钟，削弱了O-RAN动态性的核心优势。本文介绍了RANPilot，这是第一个通过主动人工智能适配来应对这一挑战的框架。RANPilot构建了一个轻量级的‘虚拟O-RAN’（一个跟踪驱动的模拟器），以在物理变化发生之前合成代表重新配置后状态的高保真训练数据，从而使人工智能模型能够提前适配。在真实世界的5G测试平台上进行的大量实验表明，RANPilot在重新配置时实现了近乎无中断的人工智能服务，与反应式基线相比，将人工智能停机时间减少了85%至94%。通过将人工智能进化范式从反应式重新开发转变为主动式准备，RANPilot探索了一种数字先行方法，以在可重构的O-RAN部署中实现强大的人工智能。
英文摘要
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.
