@inproceedings{aa69052374e24bd8b3a8247c21c85424,
title = "Efficient TIS Sensitivity Measurement with Machine Learning Approach and 5G Dataset",
abstract = "Total isotropic sensitivity (TIS) measurement is strongly required by the industry, but the procedure takes a long time. We explore a machine learning (ML) approach to speed up TIS test procedure. The experiments are conducted using 5G devices and frequency bands. The results show that our methodology can improve measurement efficiency by 35% to 65%, while still maintain high accuracy within 1 dB deviation from standard procedure. Disconnection is a critical issue during TIS measurement, so we design a calibration mechanism to reduce the risk of disconnection. Our approach can be applied widely to different system configurations. It supports not only 5G but also previous generations on different frequency bands.",
keywords = "5G, TIS, machine learning, sensitivity measurement",
author = "Chen, {Yi Wei} and Tsai, {Min Je} and Lu, {Henry Horng Shing} and Feng, {Kai Ten} and Lee, {Ta Sung} and Lan, {Jih Chuan}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 21st IEEE Consumer Communications and Networking Conference, CCNC 2024 ; Conference date: 06-01-2024 Through 09-01-2024",
year = "2024",
doi = "10.1109/CCNC51664.2024.10454722",
language = "English",
series = "Proceedings - IEEE Consumer Communications and Networking Conference, CCNC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1032--1033",
booktitle = "2024 IEEE 21st Consumer Communications and Networking Conference, CCNC 2024",
address = "美國",
}