用可解释模型实时验证开放无线网络中的强化学习调度行为。
On AI Verification in Open RAN
- 用决策树构建轻量级验证器，实现实时行为检查。
- 相比传统方法，验证延迟显著降低，适合运行时部署。
- 为多厂商环境下的可信AI提供可扩展验证架构。
开放无线接入网（Open RAN）引入了灵活的云原生架构，支持人工智能/机器学习驱动的跨异构、多厂商部署自动化。尽管可解释人工智能（XAI）有助于缓解模型黑箱问题，但可解释性本身不足以保障网络可靠运行。本文提出一种基于可解释模型的轻量级验证方法，用于验证开放无线网络中深度强化学习（DRL）代理在切片与调度任务中的行为。具体而言，采用基于决策树（DT）的验证器，在运行时进行近实时的一致性检查，而这一能力在计算开销大的现有验证方法中难以实现。我们分析了XAI与AI验证的现状，提出了可扩展的架构集成方案，并通过基于决策树的切片验证器展示了可行性。同时，也指出了未来确保开放无线网络中可信AI应用的关键挑战。
原文摘要 · Abstract (English)
Open RAN introduces a flexible, cloud-based architecture for the Radio Access Network (RAN), enabling Artificial Intelligence (AI)/Machine Learning (ML)-driven automation across heterogeneous, multi-vendor deployments. While EXplainable Artificial Intelligence (XAI) helps mitigate the opacity of AI models, explainability alone does not guarantee reliable network operations. In this article, we propose a lightweight verification approach based on interpretable models to validate the behavior of Deep Reinforcement Learning (DRL) agents for RAN slicing and scheduling in Open RAN. Specifically, we use Decision Tree (DT)-based verifiers to perform near-real-time consistency checks at runtime, which would be otherwise unfeasible with computationally expensive state-of-the-art verifiers. We analyze the landscape of XAI and AI verification, propose a scalable architectural integration, and demonstrate feasibility with a DT-based slice-verifier. We also outline future challenges to ensure trustworthy AI adoption in Open RAN.
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