Using vision-based object detection for link quality prediction in 5.6-GHz channel

EURASIP Journal on Wireless Communications and Networking

Kohei Mizuno¹  Takeru Inoue¹  Riichi Kudo¹  Kahoko Takahashi¹ 

[1] NTT Network Innovation Laboratories, Hikarinooka, 239-0847, Yokosuka-Shi, Kanagawa, Japan;

关键词: Link quality prediction;    Machine learning;    Object detection;    Wireless LAN;   

DOI  :  10.1186/s13638-020-01829-8

来源: Springer

【 摘 要 】

Various smart connected devices are emerging like automated driving cars, autonomous robots, and remote-controlled construction vehicles. These devices have vision systems to conduct their operations without collision. Machine vision technology is becoming more accessible to perceive self-position and/or the surrounding environment thanks to the great advances in deep learning technologies. The accurate perception information of these smart connected devices makes it possible to predict wireless link quality (LQ). This paper proposes an LQ prediction scheme that applies machine learning to HD camera output to forecast the influence of surrounding mobile objects on LQ. The proposed scheme utilizes object detection based on deep learning and learns the relationship between the detected object position information and the LQ. Outdoor experiments show that LQ prediction proposal can well predict the throughput for around 1 s into the future in a 5.6-GHz wireless LAN channel.

【 授权许可】

CC BY