Paper Info
Title
Online Trainable Wireless Link Quality Prediction System Using Camera Imagery
Abstract
Machine-learning-based prediction of future wireless link quality is an emerging technique that can potentially improve the reliability of wireless communications, especially at higher frequencies (e.g., millimeter-wave and terahertz technologies), through predictive handover and beamforming to solve lineof-sight (LOS) blockage problem. In this study, a real-time online trainable wireless link quality prediction system was proposed; the system was implemented with commercially available laptops. The proposed system collects datasets, updates a model, and infers the received power in real-time. The experimental evaluation was conducted using 5 GHz Wi-Fi, where received signal strength could be degraded by 10 dB when the LOS path was blocked by large obstacles. The experimental results demonstrate that the prediction model is updated in real-time, adapts to the change in environment, and predicts the time-varying Wi-Fi received power accurately.
Year
DOI
Venue
2020
10.1109/GCWkshps50303.2020.9367396
2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS)
Keywords
DocType
ISSN
mm-wave, machine learning, camera assisted, implementation, 5G, beyond 5G
Conference
2166-0069
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Sohei Itahara102.03
Takayuki Nishio210638.21
Masahiro Morikura318463.42
Koji Yamamoto413545.58