面向智能铁路的无线网络数字孪生关键技术

Ke Guan , Xinghai Guo , Danping He , Philipp Svoboda , Marion Berbineau , Stephen Wang , Bo Ai , Zhangdui Zhong , Markus Rupp
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引用次数: 0

摘要

一种名为智能铁路的新兴铁路技术有望提高运输效率、提升服务舒适度和生态友好性。智能铁路预计将整合第五代移动通信(5G)、人工智能(AI)和其他技术,这给铁路无线网络的建设、运营和维护带来了新的问题。近年来兴起的无线数字孪生(DTs)作为一种新的无线网络设计范式,可以解决这些问题,实现铁路无线网络的全生命周期管理。然而,面向铁路的无线 DT 仍面临许多科学问题和挑战。本文介绍并阐述了解决这些问题的相关关键技术,包括材料的物理-电磁特性表征、三维(3D)环境模型的自主重建、人工智能赋能的环境认知、光线跟踪(RT)、基于模型和人工智能的 RT 加速以及多光谱传感数据的生成。此外,本文还介绍了我们对各项关键技术的研究成果,并介绍了我们实验室开发的基于高性能 RT 的无线网络规划和优化系统。本文概述了实现智能铁路无线 DT 的框架,为未来研究提供了方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Key technologies for wireless network digital twin towards smart railways

An emerging railway technology called smart railway promises to deliver higher transportation efficiency, enhanced comfort in services, and greater eco-friendliness. The smart railway is expected to integrate fifth-generation mobile communication (5G), Artificial Intelligence (AI), and other technologies, which poses new problems in the construction, operation and maintenance of railway wireless networks. Wireless Digital Twins (DTs), which have recently emerged as a new paradigm for the design of wireless networks, can address these problems and enable the whole lifecycle management of railway wireless networks. However, there are still many scientific issues and challenges for railway-oriented wireless DT. Relevant key technologies to solve these problems are introduced and described, including characterization of materials' physical-EM properties, autonomous reconstruction of Three-dimensional (3D) environment model, AI-empowered environmental cognition, Ray-Tracing (RT), model-based and AI-based RT acceleration, and generation of multi-spectra sensing data. Moreover, this paper presents our research results for each key technology and describes the wireless network planning and optimization system based on high-performance RT developed by our laboratory. This paper outlines the framework for realizing the wireless DT of smart railways, providing the direction for future research.

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