Soft Information-Based Localization for 5G Networks and Beyond

被引:11
|
作者
Morselli, Flavio [1 ,2 ]
Razavi, Sara Modarres [2 ]
Win, Moe Z. [3 ]
Conti, Andrea [1 ,2 ]
机构
[1] Univ Ferrara, Dept Engn, I-44122 Ferrara, Italy
[2] Univ Ferrara, CNIT, I-44122 Ferrara, Italy
[3] MIT, Lab Informat & Decis Syst LIDS, Cambridge, MA 02139 USA
基金
欧盟地平线“2020”;
关键词
Localization; 5G; machine learning; wireless networks; TRACKING; MODEL; ALGORITHMS; PREDICTION; INTERNET; THINGS; ERROR;
D O I
10.1109/TWC.2023.3275122
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Accurate location information is crucial for a variety of new verticals and use cases enabled by 5th generation (5G) wireless networks. While existing localization techniques for cellular networks are continuously evolving in the standardization process, the stringent key performance indicator requirements defined by the 3rd Generation Partnership Project (3GPP) have not been met yet. This paper first provides an in-depth review of the standardized reference signals and time/ angle-based measurements that can be used for localization of user equipments in current 5G networks. Then, the paper details the development of a soft information (SI)-based approach that significantly improves localization accuracy for 5G and beyond 5G networks. Results are obtained in full conformity with 3GPP standards in two standardized scenarios, namely urban microcell and indoor open office, using time/angle-based measurements. Results show that the proposed SI-based localization methods significantly outperform existing techniques, especially when harsh propagation conditions and higher (millimeter Waves) frequencies are considered, paving the way to new services and performance enhancements in 5G and beyond wireless networks.
引用
收藏
页码:9923 / 9938
页数:16
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