inRAN：面向开放无线接入网络的可解释在线贝叶斯学习框架
inRAN: Interpretable Online Bayesian Learning for Network Automation in Open Radio Access Networks
AI总结：
inRAN通过可解释的替代模型和安全优化求解器，实现Open RAN网络自动化中的可解释在线贝叶斯学习，有效应对动态网络环境。
摘要：
新兴的AI/ML技术在自动化开放无线接入网络（Open RAN）的网络控制中展现出巨大潜力。然而，现有方法严重依赖于由深度神经网络参数化的黑箱策略，这种策略本质上缺乏可解释性、可解释性和透明性，并在实际网络部署中造成了重大障碍。本文提出inRAN，一种用于Open RAN网络自动化的新型可解释在线贝叶斯学习框架。核心思想是将可解释的替代模型和安全优化求解器相结合，持续优化控制动作，同时适应现实网络中的非平稳动态。我们通过三个关键组件实现了inRAN框架：1）通过集成Kolmogorov-Arnold网络（KANs）的可解释替代模型；2）通过整合遗传搜索和信任区域下降法的安全优化求解器；3）通过持续模型学习和自适应阈值偏移的在线动态追踪器。我们将在端到端的O-RAN兼容网络测试床上实现inRAN，并进行广泛的空中接口实验，重点关注网络切片的使用场景。结果表明，inRAN在保证基于概率的约束方面优于现有最先进方法，通过在在线网络控制中使用比较资源的使用情况，以92.67%的保证比率实现了对不可预见的时间演变网络动态的应对。
英文摘要：
Emerging AI/ML techniques have been showing great potential in automating network control in open radio access networks (Open RAN). However, existing approaches heavily rely on blackbox policies parameterized by deep neural networks, which inherently lack interpretability, explainability, and transparency, and create substantial obstacles in practical network deployment. In this paper, we propose inRAN, a novel interpretable online Bayesian learning framework for network automation in Open RAN. The core idea is to integrate interpretable surrogate models and safe optimization solvers to continually optimize control actions, while adapting to non-stationary dynamics in real-world networks. We achieve the inRAN framework with three key components: 1) an interpretable surrogate model via ensembling Kolmogorov-Arnold Networks (KANs); 2) safe optimization solvers via integrating genetic search and trust-region descent method; 3) an online dynamics tracker via continual model learning and adaptive threshold offset. We implement inRAN in an end-to-end O-RAN-compliant network testbed, and conduct extensive over-the-air experiments with the focused use case of network slicing. The results show that, inRAN substantially outperforms state-of-the-art works, by guaranteeing the chance-based constraint with a 92.67% assurance ratio with comparative resource usage throughout the online network control, under unforeseeable time-evolving network dynamics.
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