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This study introduces a novel Integrated Sensing and Communication (ISAC) framework tailored for railway intrusion detection, leveraging Channel State Information (CSI) from 5G-Advanced and 6G systems. By generating 22,695 CSI matrices in a 3D-rendered railway environment, the researchers developed a hybrid machine learning model that combines a 3D Convolutional Neural Network and a Bidirectional Long Short-Term Memory network to accurately detect intruders and predict their position, velocity, and time to collision. The model achieved an impressive 99.57% detection accuracy and a Mean Absolute Error of 0.4240, showcasing the efficacy of CSI-based ISAC in enhancing railway safety.
Achieving over 99% accuracy in detecting railway intrusions could revolutionize safety protocols in rail transport.
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.
