Radio Quality Clustering to Induce the Behavior Toward Optimal Wireless Connection

Abstract

Smart devices such as smartphones mount the wireless communication function by standard features. Although this function is necessary in modern society, the wireless devices sometimes have serious radio quality problems. They include connection impossibly and insufficient transmission speed. It takes long time and much human resources to solve such problems with analyzing each wireless device. Machine learning technologies are utilized in data mining issues and provide possible solutions for several problems. An unsupervised learning method especially is able to reduce the cost of collecting right data and analysis because it doesn’t need the right data. This study proposes the method identifies wireless devices which have radio quality problems by classifying the devices with an unsupervised learning technology from radio information of Wireless LAN. The evaluation experiment on three kinds of unsupervised learning technology with selecting features of radio quality worsen is also described. Gaussian mixture model showed the highest precision accuracy.