超5G网络中位置感知服务的体系结构、协议和算法

P. Hammarberg, Julia Vinogradova, Gábor Fodor, Ritesh Shreevastav, S. Dwivedi, F. Gunnarsson
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引用次数: 3

摘要

汽车和铁路行业正在迅速转型,大力推动自动化和数字化,目标是提高便利性、安全性、效率和可持续性。由于辅助和全自动化的汽车和火车运输服务越来越依赖于车到物的通信和高精度实时定位,因此有必要持续保持高精度定位,即使在隧道、城市峡谷或茂密树叶覆盖的区域等遮挡场景中也是如此。在本文中,我们从方法和架构方面回顾了第三代合作伙伴项目的5G定位框架,并提出了增强措施,以满足运输行业的严格要求。特别是,我们强调了融合蜂窝和传感器测量的好处,并讨论了在网络端实现这一点所需的架构和协议支持。我们还提出了一种定位框架,将蜂窝网络测量与机载传感器的测量相融合。我们通过一个数值例子说明了所提出的基于融合的定位方法的可行性。
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Architecture, Protocols, and Algorithms for Location-Aware Services in Beyond 5G Networks
The automotive and railway industries are rapidly transforming with a strong drive toward automation and digitalization, with the goal of increased convenience, safety, efficiency, and sustainability. Since assisted and fully automated automotive and train transport services increasingly rely on vehicle-to-everything communications and high-accuracy real-time positioning, it is necessary to continuously maintain high-accuracy localization, even in occlusion scenes such as tunnels, urban canyons, or areas covered by dense foliage. In this article, we review the 5G positioning framework of the 3rd Generation Partnership Project in terms of methods and architecture and propose enhancements to meet the stringent requirements imposed by the transport industry. In particular, we highlight the benefit of fusing cellular and sensor measurements, and discuss required architecture and protocol support for achieving this at the network side. We also propose a positioning framework to fuse cellular network measurements with measurements by onboard sensors. We illustrate the viability of the proposed fusion-based positioning approach using a numerical example.
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来源期刊
CiteScore
10.80
自引率
0.00%
发文量
55
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