An Experimental Study of Harvesting Channel State Information of WiFi Signals

With the pervasive wireless communication networks and devices, WiFi has become an indispensable part of our daily life. The features of WiFi signals are commonly characterized using the received signal strength indication (RSSI) and the channel state information (CSI). In recent years, many machine learning algorithms have been proposed to analyze CSI of WiFi signals for various applications, such as indoor localization and gesture recognition. In this paper, we conduct an experimental study of harvesting channel state information of WiFi signals and evaluate the impact of various factors on the quality of these CSI data. Our measurement results show the randomness and the inefficiency of the collected CSI samples based on data visualization. Our study may stimulate more attention on the repeatability of the networking experiments and call for more open data initiatives to accelerate the applications of machine learning techniques in networking research.