Positioning in 5G and 6G Networks-A Survey

被引:38
|
作者
Mogyorosi, Ferenc [1 ]
Revisnyei, Peter [1 ]
Pasic, Azra [1 ]
Papp, Zsofia [2 ]
Toros, Istvan [2 ]
Varga, Pal [1 ]
Pasic, Alija [1 ]
机构
[1] Budapest Univ Technol & Econ, Dept Telecommun & Media Informat, Muegyet Rkp 3, H-1111 Budapest, Hungary
[2] Ericsson Res, H-1117 Budapest, Hungary
关键词
positioning techniques; machine learning; 5G; 6G; network-based positioning; indoor positioning; asset tracking; positioning use cases; AUGMENTED REALITY; COMMUNICATION; LOCALIZATION; TECHNOLOGIES; SYSTEMS; ARCHITECTURE; ALGORITHMS; SAFETY;
D O I
10.3390/s22134757
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning-indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios.
引用
收藏
页数:25
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