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Deep learning-based high-speed railway communication systems – Scientific Reports

References

  1. Hu, F., Ling, Z., Liu, T., Li, H. & Ai, B. Wireless perception of high-speed railway communication: Challenges, framework, and future directions. IEEE Wireless Commun. 31, 284–292 (2024).

    Google Scholar 

  2. Wu, Y., Jia, H., Nakawaki, Y., Pan, Z. & Shimamoto, S. Doppler-only wireless positioning for high-speed railway based on fractional doppler estimation. In 2024 IEEE Wireless Communications and Networking Conference (WCNC), 1–6 (IEEE, 2024).

  3. Salim, S. I. et al. A cost effective real time rail track monitoring system leveraging multi sensor fusion and multi objective optimization. Sci. Rep. 15, 31728 (2025).

    Google Scholar 

  4. Vlachos, E., Mavrokefalidis, C., Berberidis, K. & Alexandropoulos, G. C. Improving wideband massive MIMO channel estimation with UAV state-space information. IEEE Transactions on Vehicular Technology https://doi.org/10.1109/tvt.2025.3569350 (2025).

    Google Scholar 

  5. Sriranga, A. K., Lu, Q. & Birrell, S. A. A deep learning-based contactless driver state monitoring radar system for in-vehicle physiological applications. IEEE Trans. Intell. Transp. Syst. 26, 9491–9499 (2025).

    Google Scholar 

  6. Le, S., Lai, Y., Wang, Y. & He, H. Deep-learning-based uncertainty-estimation approach for unknown traffic identification. IEEE Trans. Artif. Intell. 6, 533–548 (2025).

    Google Scholar 

  7. Zhou, T. et al. A cluster-based dynamic narrow-beam channel model for vehicle-to-infrastructure communications. IEEE Trans. Wireless Commun. 23, 15858–15871 (2024).

    Google Scholar 

  8. Sachan, A. & Kumar, N. SDVN enabled traffic light cooperative framework for E-SIoV mobility in a smart city scenario. IEEE Trans. Veh. Technol. 73, 10990–11001 (2024).

    Google Scholar 

  9. Dong, Q.-S., Zhang, S.-Y., Shahrrava, B. & Zheng, H. An enhanced channel estimation scheme in OFDM-IM systems with index pilots for IEEE 802.11 p standard. IEEE Access 12, 74389–74405 (2024).

    Google Scholar 

  10. Du, J. et al. Indoor vehicle positioning for MIMO-OFDM WiFi systems via rearranged sparse Bayesian learning. IEEE Transactions on Wireless Communications 23, 7849–7864 (2023).

    Google Scholar 

  11. Liu, X., Liao, T., Yang, L., Luo, Z. & Yue, G. Measurements and analysis of millimeter-wave propagation for 500 km/h ultra-high-speed maglev train communications between train and trackside. IEEE Internet Things J. 12, 33167–33182 (2025).

    Google Scholar 

  12. Zhang, Z. et al. Channel measurements and modeling for dynamic vehicular isac scenarios at 28 Ghz. IEEE Trans. Commun. 73, 6884–6897 (2025).

    Google Scholar 

  13. Zhang, Y. et al. Non-stationarity characterization and geometry-cluster-based stochastic model for high-speed train radio channels. IEEE Trans. Intell. Transp. Syst. 24, 7122–7137 (2023).

    Google Scholar 

  14. Zhang, Z., Sun, R., Sun, Z., Yi, Z. & Liu, L. Dynamic cluster kernel-based channel modeling algorithm for high-speed railway scenarios. AEU-International Journal of Electronics and Communications 156080 (2025).

  15. Yunyun, Z. Security state estimation algorithm of data transmission channel in wireless communication network. In 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), 12–16 (IEEE, 2022).

  16. Zhang, Q., Zhao, Y., Dong, H. & Zhao, J. A reconstruction and recovery network-based channel estimation in high-speed railway wireless communications. Digital Communications and Networks (2024).

  17. Li, Q., Yuan, J., Qiu, M., Li, S. & Xie, Y. Low complexity turbo SIC-MMSE detection for orthogonal time frequency space modulation. IEEE Trans. Commun. 72, 3169–3183 (2024).

    Google Scholar 

  18. Miao, Z. & Liao, Q. The application of Kalman filter algorithm in rail transit signal safety detection. IEEE Access 13, 91480–91493 (2025).

    Google Scholar 

  19. Van Chien, T. et al. Single-and multi-objective stochastic optimization for next-generation networks in the generative AI and quantum computing era. arXiv preprint arXiv:2601.02175 (2026).

  20. Abdelsattar, M., Amer, E. S., Ziedan, H. A. & Salama, W. M. CNN-LSTM-AM approach for outdoor wireless optical communication systems. Sci. Rep. 15, 32178 (2025).

    Google Scholar 

  21. Tung, N. X. et al. Graph neural networks for next-generation-IoT: Recent advances and open challenges. IEEE Communications Surveys & Tutorials (2025).

  22. Hu, R. et al. Deep learning-based channel estimation with low-density pilot in MIMO-OFDM systems. In IEEE International Conference on Communications, 2619–2624 (2023).

  23. Xu, M., Zhang, S., Ma, J. & Dobre, O. A. Deep learning-based time-varying channel estimation for RIS assisted communication. IEEE Commun. Lett. 26, 94–98 (2021).

    Google Scholar 

  24. Petrov, T. et al. An analytical approach to the estimation of vehicular communication reliability for intersection control applications. Veh. Commun. 45, 100693 (2024).

    Google Scholar 

  25. Zohir, H. M., Ismael, I. M., El-Gendy, E. M. & Saafan, M. M. Advancements in accident-aware traffic management: A comprehensive review of V2X-based route optimization. Sci. Rep. 15, 35041 (2025).

    Google Scholar 

  26. Li, J. et al. Effective joint scheduling and power allocation for URLLC-oriented V2I communications. IEEE Trans. Veh. Technol. 73, 11694–11705 (2024).

    Google Scholar 

  27. Li, T.-H., Khandaker, M. R. A., Tariq, F., Wong, K.-K. & Khan, R. T. Learning the wireless V2I channels using deep neural networks. In 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 1–5 (2019).

  28. Zou, C., Yang, F., Song, J. & Han, Z. Channel autoencoder for wireless communication: State of the art, challenges, and trends. IEEE Commun. Mag. 59, 136–142 (2021a).

    Google Scholar 

  29. Nguyen, V. D., Ha, D. V., Duong, V. V., Le, H. A. & Nguyen, T. H. Joint fast time domain channel estimation with ici cancellation for LTE-R systems. Physical Communication https://doi.org/10.1016/j.phycom.2021.101349 (2021).

    Google Scholar 

  30. Siriwanitpong, A., Sanada, K., Hatano, H., Mori, K. & Boonsrimuang, P. Deep learning-based channel estimation with 1D CNN for OFDM systems under high-speed railway environments. IEEE Access https://doi.org/10.1109/access.2025.3531009 (2025).

    Google Scholar 

  31. Liu, R. et al. 6G enabled advanced transportation systems. IEEE Trans. Intell. Transp. Syst. 25, 10564–10580 (2024).

    Google Scholar 

  32. Berbineau, M. et al. Channel models for performance evaluation of wireless systems in railway environments. IEEE Access 9, 45903–45918 (2021).

    Google Scholar 

  33. Zhong, H., Wang, K., Li, W., Burris, M. W. & Sinha, K. C. An urban-rural divide? Preferences for autonomous vehicles in small and med-sized metropolitan areas. Appl. Geogr. 169, 103324 (2024).

    Google Scholar 

  34. Aja-Fernández, S., Ramos-Llordén, G. & Yushkevich, P. A. Chapter 5 – image representation and 2d signal processing. In Frangi, A. F., Prince, J. L. & Sonka, M. (eds.) Medical Image Analysis, The MICCAI Society book Series, 115–143 (Academic Press, 2024).

  35. Hong, Y., Thaj, T. & Viterbo, E. Chapter 7 – channel estimation methods. In Delay-Doppler Communications (eds Hong, Y. et al.) 153–175 (Academic Press, 2022).

  36. Dehbi, M. et al. V2i communication and multi-sensor fusion for real-time accurate localization. In GLOBECOM 2024-2024 IEEE Global Communications Conference, 3225–3230 (IEEE, 2024).

  37. Yun, Z. & Han, S. Towards a real-time wireless powered communication network: Design, implementation and evaluation. In 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS), 347–359 (2021).

  38. Vijayakumar, S., Flynn, R., Corcoran, P. & Murray, N. Predicting quality of multimedia experience using electrocardiogram and respiration signals. IEEE Access (2024).

  39. Xie, H., Qin, Z. & Tao, X. & Han, Z (Large model empowered semantic communications, 2024).

    Google Scholar 

  40. S, H. Y., S, B. T., R, S. G., N, R. K. K. & Karjigi, V. Wireless communication using autoencoder. In 2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES) (2023).

  41. Zou, C., Yang, F., Song, J. & Han, Z. Channel autoencoder for wireless communication: State of the art, challenges, and trends. IEEE Commun. Mag. 59, 136–142 (2021b).

    Google Scholar 

  42. Ouyang, Y. et al. Next decade of telecommunications artificial intelligence. CAAI Artif. Intell. Res. 1, 28–53 (2022).

    Google Scholar 

  43. Liao, Y., Sun, G., Cai, Z., Shen, X. & Huang, Z. Nonlinear Kalman filter-based robust channel estimation for high mobility OFDM systems. IEEE Trans. Intell. Transp. Syst. 22, 7219–7231 (2020).

    Google Scholar 

  44. Tu, C.-L. et al. Synchronization and channel estimation design for multi-stream mimo system in sub-terahertz channel model. IEEE Open Journal of Circuits and Systems (2024).

  45. Karnna, C., Udomsiri, S. & Phrompichai, S. Channel estimation based on pilot signal and iterative method for tdd based massive mimo systems. In 2022 International Electrical Engineering Congress (iEECON), 1–4 (2022).

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