Deep Learning-based EMI and IEMI Classification in 5G-R High-Speed Rail Wireless Communications

The proliferation of high-speed rail (HSR) networks and railway electrification has advanced the integration of the latest wireless communication networks with railway systems. Ensuring a reliable bidirectional communication link between moving trains and base stations is crucial to maintaining the safety of real-time rail operations. However, the growing complexity of railway systems and increased exposure to electromagnetic emissions present substantial challenges. In particular, railway wireless communication networks are vulnerable to various kinds of electromagnetic interference (EMI) and intentional EMI (IEMI), which could cause operational disruptions and safety hazards. This paper proposes a real-time classification method for EMI and IEMI, using deep learning-based bidirectional long-short-term memory (BiLSTM) networks. By employing multivariate time-series characteristics, the method can simultaneously learn both time and frequency information at a finer resolution, offering better performance than existing methods. The simulation results demonstrate a high accuracy of 93.4% and adaptability at different speeds and in various scenarios.