[time]
2023-08-31T22:18:00+02:00

[track]
8

[team_name]
ISCAS

[team_institution]
Institute of Software, Chinese Academy of Sciences

[logolink]


[team_members]


[reference_person]
Chang Su, Fusang Zhang, Beihong jin

[reference_email]
suchang21@otcaix.iscas.ac.cn

[description_short]
5G positioning will be part of our lives and it will enable diversified applications in all walks of life. A large number of application scenarios such as the Internet of Vehicles, autonomous driving, smart manufacturing, smart logistics, drones, and asset tracking have higher requirements for positioning capabilities. Excessive errors may lead to poor user experience or other problems. Therefore, we should enhance network positioning technology to improve 5G positioning accuracy.

The challenges we face in Track 8 are the features of raw data provided are inaccurate. These features are the results of processing and it’s so hard to infer more information from these features. What’s more, there exists a mixture of LOS paths, weak LOS paths, and NLOS paths. And there are existing timing errors among the receivers in TRPs, called time alignment errors (TAEs). The TAEs of the TRPs are unknown and different in different datasets. All of the above have caused great difficulties in 

[description_long_link]
https://github.com/sulima7/IPIN-T8/tree/main

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true

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