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

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...

Full description

Saved in:
Bibliographic Details
Published in2017 IEEE International Conference on Communications (ICC) pp. 1 - 6
Main Authors Xuyu Wang, Xiangyu Wang, Shiwen Mao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
angle of arriving (AOA)
Antenna measurements
Antennas
channel state information
deep convolutional neural networks
deep learning
Indoor localization
Neural networks
OFDM
phase difference
Phase measurement
Training
Wireless fidelity
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:1938-1883
DOI:10.1109/ICC.2017.7997235