Abstract
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.
Highlights
Connected devices are improving our lives, and their roles are expanding with the rapid enhancement of wireless communication systems [1]
Experiments using HD cameras and wireless Local area network (LAN) systems are conducted in an outdoor environment, and link quality (LQ) prediction performance is evaluated by using the relationship between the object bounding-box information and the throughput of super high frequency (SHF) channels (5.6 GHz)
5.1 LQ prediction in time domain The normalized throughput was predicted by using the input feature set, ΩBV[t0], provided by M2Det with Srecog of 0.5 and TF of 1.0 [s]
Summary
Connected devices are improving our lives, and their roles are expanding with the rapid enhancement of wireless communication systems [1]. Yang et al [17] proposed resource management to realize ultra-reliable and low-latency wireless communication for connected vehicles, and Almeida et al [18] proposed a quality of service estimator using UAV base station positions and user traffic demand Since these works do not consider the influence of the surrounding objects, the LQ change caused by large mobile objects such as trucks cannot be predicted. Experiments using HD cameras and wireless LAN systems are conducted in an outdoor environment, and LQ prediction performance is evaluated by using the relationship between the object bounding-box information and the throughput of SHF channels (5.6 GHz). The key contribution of this paper is to introduce the two-step LQ prediction that can take advantage of subsequent advances in object detection algorithms and yield explainable AI results by using the bounding-box information. Δ′Φχ,j corresponds to a longer time average than ΔΦχ,j
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