为6G设计可解释的AI原生基站，提升关键通信的透明度与可靠性。
XAI-on-RAN: Explainable, AI-native, and GPU-Accelerated RAN Towards 6G
- 构建权衡解释性、延迟与显卡利用率的数学模型
- 混合式XAI模型在性能上优于传统基线模型
- 适合医疗、工业自动化等高安全要求场景
人工智能原生无线接入网（RAN）将为智能电网、自动驾驶、远程医疗、工业自动化等垂直行业提供服务。现代5G/6G设计日益依赖AI进行网络优化，但AI决策的不透明性在关键领域带来风险。这些应用常通过非公共网络（NPN）或专用网络切片交付，可靠性和安全性至关重要。本文基于3GPP对非公共网络的愿景，强调在医疗、工业自动化和机器人等高风险通信中实现透明可信AI的必要性。我们设计了一种数学框架，用于建模可解释AI（XAI）部署中解释精度、公平性、延迟与GPU利用率之间的权衡。实证评估表明，所提出的混合XAI模型xAI-Native在性能上持续优于传统基线模型。
原文摘要 · Abstract (English)
Artificial intelligence (AI)-native radio access networks (RANs) will serve vertical industries with stringent requirements: smart grids, autonomous vehicles, remote healthcare, industrial automation, etc. To achieve these requirements, modern 5G/6G design increasingly leverage AI for network optimization, but the opacity of AI decisions poses risks in mission-critical domains. These use cases are often delivered via non-public networks (NPNs) or dedicated network slices, where reliability and safety are vital. In this paper, we motivate the need for transparent and trustworthy AI in high-stakes communications (e.g., healthcare, industrial automation, and robotics) by drawing on 3rd generation partnership project (3GPP)'s vision for non-public networks. We design a mathematical framework to model the trade-offs between transparency (explanation fidelity and fairness), latency, and graphics processing unit (GPU) utilization in deploying explainable AI (XAI) models. Empirical evaluations demonstrate that our proposed hybrid XAI model xAI-Native, consistently surpasses conventional baseline models in performance.
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