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Rahmani, Shahpour, Yazdani, Nasser. (1405). 5G-R Framework for High-Speed Rail Connectivity Using LSTM and Multi-Access Edge Computing. نشریه علمی دانشگاه سیستان و بلوچستان, 9(3), 281-296. doi: 10.22111/ieco.2025.52762.1709
Shahpour Rahmani; Nasser Yazdani. "5G-R Framework for High-Speed Rail Connectivity Using LSTM and Multi-Access Edge Computing". نشریه علمی دانشگاه سیستان و بلوچستان, 9, 3, 1405, 281-296. doi: 10.22111/ieco.2025.52762.1709
Rahmani, Shahpour, Yazdani, Nasser. (1405). '5G-R Framework for High-Speed Rail Connectivity Using LSTM and Multi-Access Edge Computing', نشریه علمی دانشگاه سیستان و بلوچستان, 9(3), pp. 281-296. doi: 10.22111/ieco.2025.52762.1709
Rahmani, Shahpour, Yazdani, Nasser. 5G-R Framework for High-Speed Rail Connectivity Using LSTM and Multi-Access Edge Computing. نشریه علمی دانشگاه سیستان و بلوچستان, 1405; 9(3): 281-296. doi: 10.22111/ieco.2025.52762.1709

5G-R Framework for High-Speed Rail Connectivity Using LSTM and Multi-Access Edge Computing

International Journal of Industrial Electronics Control and Optimization
مقاله 3، دوره 9، شماره 3، آذر 2026، صفحه 281-296    اصل مقاله (873.43 K)
نوع مقاله: Research Articles
شناسه دیجیتال (DOI): 10.22111/ieco.2025.52762.1709
نویسندگان
Shahpour Rahmani1؛ Nasser Yazdani* 2
1School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran Department of Computer Sciences, Faculty of Mathematics, Statistics and Computer Science, University of Sistan and Baluchestan, Zahedan, Iran
2School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
چکیده
High-speed rail systems, operating at speeds up to 350 km/h, face significant challenges in delivering reliable network connectivity due to frequent handovers, signal degradation, and network congestion. This paper proposes the 5G-R framework, an optimized solution integrating beamforming, network slicing, railway-specific Long Short-Term Memory (LSTM) algorithms, and Multi-Access Edge Computing (MEC) to enhance connectivity performance. By leveraging real-time train data, such as speed and GPS location, the framework optimizes handover prediction and traffic management, achieving robust performance in diverse environments. Compared to 4G LTE and standard 5G, the 5G-R framework demonstrates significant improvements, including a 250 Mbps throughput, 15 ms latency, and 95% handover success rate. Network slicing optimizes resource allocation, reducing congestion by approximately 30%, while MEC enables low-latency control for train systems. Field trials along the Beijing-Zhangjiakou railway (174 km, urban/suburban) and simulations validate the framework’s adaptability across urban and rural routes. Designed for compatibility with the Future Railway Mobile Communication System (FRMCS), the 5G-R framework lays a foundation for future advancements, including 6G and satellite communications. Future research should focus on optimizing performance in extreme environments and densely populated routes to support autonomous transport systems. This optimization-driven approach establishes a scalable model for next-generation rail communication systems.
کلیدواژه‌ها
5G-R؛ FRMCS؛ LSTM؛ MEC؛ Optimization
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