CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi

Abstract

With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. First, by leveraging a modified device driver, we extract phase data of channel state information (CSI), which is used to estimate angle of arriving (AOA). We then create estimated AOA images as input to the DCNN, to train the weights in the offline phase. The location of mobile device is predicted based on the trained DCNN and new CSI AOA images. We implement the proposed CiFi system with commodity Wi-Fi devices in the 5GHz band and verify its performance with extensive experiments in two representative indoor environments.