AI 中文总结
本研究提出结合3D CNN与BiLSTM的机器学习模型，利用6G ISAC的CSI技术实现铁路入侵检测与碰撞预测，在合成数据上达到99.57%的检测准确率及0.4240的组合MAE。
AI 中文摘要
一体化感知通信（ISAC）将感知与通信相结合，以高效利用无线资源，正成为下一代无线网络的关键范式。借助5G-Advanced和6G系统的宽带宽、高频段以及大规模天线阵列，ISAC可利用信道状态信息（CSI）实现物理层感知。第三代合作伙伴计划（3GPP）的第19版确定了32种潜在的ISAC用例，尤其强调对移动物体的检测与跟踪。本研究针对铁路入侵检测的感知用例展开，其中包括野生动物在内的入侵者进入铁路轨道可能引发严重碰撞风险。我们利用三维渲染铁路环境与Sionna无线电模拟器，生成了22695个带有对应真值的CSI矩阵。我们开发了一种结合三维卷积神经网络（3D CNN）与双向长短期记忆（BiLSTM）网络的机器学习模型，用于检测轨道危险区内的入侵者，并估算其相对于列车的实时位置、速度及碰撞时间。在合成CSI数据上，该模型在平衡测试集上实现了99.57%的入侵者检测准确率，且位置、速度与碰撞时间预测的组合平均绝对误差（MAE）为0.4240。这些结果证明了基于CSI的ISAC感知结合机器学习在可靠铁路入侵检测方面的潜力。CSI生成、预处理及模型开发的完整代码库可在该httpsURL公开获取。
英文摘要
Integrated Sensing and Communication (ISAC) combines sensing and communication to efficiently utilize wireless resources and is emerging as a key paradigm for next-generation wireless networks. By leveraging the wide bandwidth, high frequencies, and massive antenna arrays of 5G-Advanced and 6G systems, ISAC enables physical-layer sensing using Channel State Information (CSI). The 3rd Generation Partnership Project (3GPP) Release 19 identifies 32 potential ISAC use cases, with particular emphasis on detecting and tracking moving objects. In this work, we address the Sensing for Railway Intrusion Detection use case, where intruders, including wildlife, entering a railway track can pose serious collision risks. We generated 22,695 CSI matrices with corresponding ground truth using a 3D-rendered railway environment and the Sionna radio simulator. We developed a machine learning model combining a three-dimensional Convolutional Neural Network (3D CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network to detect intruders in the track danger zone and estimate their real-time position relative to the train, velocity, and time to collision. On synthetic CSI data, the model achieves 99.57% intruder-detection accuracy on a balanced test set and a combined Mean Absolute Error (MAE) of 0.4240 for position, velocity, and time-to-collision prediction. These results demonstrate the potential of CSI-based ISAC sensing with machine learning for reliable railway intrusion detection. The complete codebase for CSI generation, preprocessing, and model development is publicly available at https://github.com/EdgeIntelligenceLab/6g-isac-railway-intrusion-detection.
