۵G-R Framework for High-Speed Rail Connectivity Using LSTM and Multi-Access Edge Computing

High-speed rail systems, operating at speeds up to ۳۵۰ km/h, face significant challenges in delivering reliable network connectivity due to frequent handovers, signal degradation, and network congestion. This paper proposes the ۵G-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 ۴G LTE and standard ۵G, the ۵G-R framework demonstrates significant improvements, including a ۲۵۰ Mbps throughput, ۱۵ ms latency, and ۹۵% handover success rate. Network slicing optimizes resource allocation, reducing congestion by approximately ۳۰%, while MEC enables low-latency control for train systems. Field trials along the Beijing-Zhangjiakou railway (۱۷۴ 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 ۵G-R framework lays a foundation for future advancements, including ۶G 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.

کلیدواژه ها:

۵G-R ، FRMCS ، LSTM ، MEC ، Optimization