AI 中文总结
研究6G智能感知与通信中无线多模态基础模型的分布外检测问题，提出WMFM - OOD框架，通过构建基站原型和温度缩放概率评分机制区分异常，在DeepVerse6G数据集验证，显著优于基线，提升检测灵敏度，保障网络可靠性。
AI 中文摘要
基础模型（FMs），如无线多模态基础模型（WMFM），集成到6G网络为智能感知与通信（ISAC）提供统一框架，利用广义表示同时优化数据传输和环境感知。但数据驱动模型在安全关键基础设施中的部署受分布外（OOD）问题阻碍。标准FMs在封闭世界假设下运行，在未见无线电环境中易出现静默故障。为解决此可靠性差距，提出基于度量的健壮OOD检测框架WMFM - OOD。它在联合潜在空间构建几何基站原型捕获有效无线电环境的流形结构，采用温度缩放概率评分机制区分分布内（ID）和协变量偏移异常。在DeepVerse6G数据集上验证，结果表明WMFM - OOD显著优于未校准基线，在最优温度状态下，接收者操作特征曲线下面积（AUROC）为0.8824，在95%真阳性率（TPR）下误报率（FPR）降低约17%，为减轻灾难性模型故障提供检测灵敏度，且不完全破坏网络可用性。
英文摘要
The integration of Foundation Models (FMs), such as the Wireless Multimodal Foundation Model (WMFM), into 6G networks provides a unified framework for Integrated Sensing and Communication (ISAC), leveraging generalized representations to simultaneously optimize data transmission and environmental perception. However, the deployment of such data-driven models in safety-critical infrastructure is hindered by the Out-of-Distribution (OOD) problem, which poses a fundamental threat to system trustworthiness. Standard FMs operate under a closed-world assumption, rendering them vulnerable to silent failures when deployed in unseen radio environments. To address this reliability gap and ensure trustworthy network operation, we propose WMFM-OOD, a robust metric-based OOD detection framework. Unlike traditional methods that rely on raw compatibility scores, WMFM-OOD constructs geometric Base Station (BS) Prototypes within the joint latent space to capture the manifold structure of valid radio environments. By employing a temperature-scaled probabilistic scoring mechanism, our approach effectively distinguishes between In-Distribution (ID) and covariate-shifted anomalies. We validate the framework on the DeepVerse6G dataset. Experimental results demonstrate that WMFM-OOD significantly outperforms uncalibrated baselines, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8824 and reducing the False Positive Rate (FPR) at 95 % True Positive Rate (TPR), commonly referred to as FPR95, by approximately 17% in the optimal temperature regime, thereby providing an initial layer of detection sensitivity to mitigate catastrophic model failures without completely disrupting network availability.
Commentspresented at IEEE VTC 2026 Fall, 4 figures
