Machine Learning-based RSSI Prediction in Factory Environments

被引：0

作者：

Webber, Julian ^{[1
,2
]}

Suga, Norisato ^{[1
,3
]}

Ano, Susumu ^{[1
]}

Jou, Yafei ^{[1
,4
]}

Mehbodniya, Abolfazl ^{[1
,5
]}

Higashimori, Toshihide ^{[1
]}

Yano, Kazuto ^{[1
]}

Suzuki, Yoshinori ^{[1
]}

机构：

[1] Adv Telecommun Res Inst Int ATR, Wave Engn Labs, Sora Ku, 2-2-2 Hikaridai, Kyoto 6190288, Japan

[2] Osaka Univ, Grad Sch Engn Sci, Toyonaka, Osaka, Japan

[3] Tokyo Univ Sci, Fac Engn, Tokyo, Japan

[4] Okayama Univ, Grad Sch Nat Sci & Technol, Okayama, Japan

[5] Kuwait Coll Sci & Technol, Kuwait, Kuwait

来源：

PROCEEDINGS OF 2019 25TH ASIA-PACIFIC CONFERENCE ON COMMUNICATIONS (APCC) | 2019年

关键词：

channel prediction; machine-learning; neuralnetwork; RSSI measurements; factory environment; anomaly detection;

D O I：

10.1109/apcc47188.2019.9026476

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：195 / 200

页数：6

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