A Weighted Random Forest Based Positioning Algorithm for 6G Indoor Communications

被引：1
|
作者：
Wu, Yang [1 ]
Wang, Yinghua [2 ]
Huang, Jie [1 ,2 ]
Wang, Cheng-Xiang [1 ,2 ]
Huang, Chen [1 ,2 ]
机构：
[1] Southeast Univ, Sch Informat Sci & Engn, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China
[2] Purple Mt Labs PML, Pervas Commun Res Ctr, Nanjing 211111, Peoples R China
来源：
2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL) | 2022年
基金：
中国博士后科学基金; 国家重点研发计划; 中国国家自然科学基金;
关键词：
6G; indoor positioning; channel state information; random forest; ray-tracing;
D O I：
10.1109/VTC2022-Fall57202.2022.10012921
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：
Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz (THz) communications have ultra-broad bandwidth so that the channel state information (CSI) will have a high resolution. In this paper, a weighted random forest (WRF) based indoor positioning algorithm using CSI-based channel fingerprint feature is proposed to achieve high-precision positioning for 6G indoor communications. In addition, ray-tracing (RT) is used to improve the efficiency of establishing channel fingerprint database. The simulation results demonstrate the accuracy and robustness of the proposed algorithm. It is shown that the positioning accuracy of the algorithm is stable within 6 cm in different indoor scenarios when the channel fingerprint database is established at 0.2 m intervals.
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页数：6
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