Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2015

Abstract

Wireless LANs, particularly WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing these applications is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to identify the existence of the Line-Of-Sight (LOS) path acts as a key enabler for adaptive communication, cognitive radios, and robust localization. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with MAC-layer received signal strength. In this paper, we propose two PHY-layer channel-statistics-based features from both the time and frequency domains. To further break away from the intrinsic bandwidth limit of WiFi, we extend to the spatial domain and harness natural mobility to magnify the randomness of NLOS paths while retaining the deterministic nature of the LOS component. We propose LiFi, a statistical LOS identification scheme with commodity WiFi infrastructure, and evaluate it in typical indoor environments covering an area of 1500 m 2 . Experimental results demonstrate that LiFi achieves an overall LOS detection rate of 90.42% with a false alarm rate of 9.34% for the temporal feature and an overall LOS detection rate of 93.09% with a false alarm rate of 7.29% for the spectral feature.

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Wireless Communications

Volume

14

Issue

11

First Page

6125

Last Page

6136

ISSN

1536-1276

Identifier

10.1109/TWC.2015.2448540

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional URL

https://doi.org/10.1109/TWC.2015.2448540

Download

Share

COinS