@inproceedings{b6ebc5ba539a47828b64db75d1f24c43,
title = "Machine learning-based RSSI prediction in factory environments",
abstract = "This paper studies the prediction of the received signal strength at a receiver that tracks an automated guided vehicle (AGV) as it moves along a factory route. We apply machine learning to predict a sliding-window pattern of the received signal strength indication (RSSI) signal and further improve the prediction performance by using multiple receivers. The performance evaluation processes wireless data collected from actual received signal strength measurement experiments recorded from an OFDM transmitter in the 2.4 GHz band. The performance is evaluated for vehicle movement along routes with both repetitive and random sections and with and without position errors.",
keywords = "Anomaly detection, Channel prediction, Factory environment, Machine-learning, Neural-network, RSSI measurements",
author = "Julian Webber and Norisato Suga and Susumu Ano and Yafei Hou and Abolfazl Mehbodniya and Toshihide Higashimori and Kazuto Yano and Yoshinori Suzuki",
note = "Funding Information: This work is supported by Japan Ministry of Internal Affairs and Communications with the fund of {"}R&D on Technologies to Densely and Efficiently Utilize Radio Resources of Unlicensed Bands in Dedicated Areas.{"} Publisher Copyright: {\textcopyright} 2019 IEEE.; 25th Asia-Pacific Conference on Communications, APCC 2019 ; Conference date: 06-11-2019 Through 08-11-2019",
year = "2019",
month = nov,
doi = "10.1109/APCC47188.2019.9026476",
language = "English",
series = "Proceedings of 2019 25th Asia-Pacific Conference on Communications, APCC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "195--200",
editor = "Bao, {Vo Nguyen Quoc} and Thanh, {Tran Thien}",
booktitle = "Proceedings of 2019 25th Asia-Pacific Conference on Communications, APCC 2019",
}