Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/APCC47188.2019.9026476 | 2019 25th Asia-Pacific Conference on Communications (APCC) |
Keywords | DocType | ISSN |
channel prediction,machine-learning,neural-network,RSSI measurements,factory environment,anomaly detection | Conference | 2163-0771 |
ISBN | Citations | PageRank |
978-1-7281-3680-6 | 0 | 0.34 |
References | Authors | |
5 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julian Webber | 1 | 1 | 1.37 |
| Norisato Suga | 2 | 0 | 1.35 |
| Susumu Ano | 3 | 0 | 0.34 |
| Yafei Hou | 4 | 12 | 11.89 |
| Abolfazl Mehbodniya | 5 | 157 | 21.39 |
| Toshihide Higashimori | 6 | 0 | 0.34 |
| Kazuto Yano | 7 | 18 | 9.01 |
| Yoshinori Suzuki | 8 | 0 | 0.34 |