WiFi-based indoor line-of-sight identification

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)...

Full description

Saved in:
Bibliographic Details
Main Authors: ZHOU, Zimu, YANG, Zheng, WU, Chenshu, SHANGGUAN, Longfei, CAI, Haibin, LIU, Yunhao, NI, Lionel M.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/4536
https://ink.library.smu.edu.sg/context/sis_research/article/5539/viewcontent/twc15_zhou.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-5539
record_format dspace
spelling sg-smu-ink.sis_research-55392019-12-26T09:11:13Z WiFi-based indoor line-of-sight identification ZHOU, Zimu YANG, Zheng WU, Chenshu SHANGGUAN, Longfei CAI, Haibin LIU, Yunhao NI, Lionel M. 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. 2015-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4536 info:doi/10.1109/TWC.2015.2448540 https://ink.library.smu.edu.sg/context/sis_research/article/5539/viewcontent/twc15_zhou.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Software Engineering
spellingShingle Software Engineering
ZHOU, Zimu
YANG, Zheng
WU, Chenshu
SHANGGUAN, Longfei
CAI, Haibin
LIU, Yunhao
NI, Lionel M.
WiFi-based indoor line-of-sight identification
description 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.
format text
author ZHOU, Zimu
YANG, Zheng
WU, Chenshu
SHANGGUAN, Longfei
CAI, Haibin
LIU, Yunhao
NI, Lionel M.
author_facet ZHOU, Zimu
YANG, Zheng
WU, Chenshu
SHANGGUAN, Longfei
CAI, Haibin
LIU, Yunhao
NI, Lionel M.
author_sort ZHOU, Zimu
title WiFi-based indoor line-of-sight identification
title_short WiFi-based indoor line-of-sight identification
title_full WiFi-based indoor line-of-sight identification
title_fullStr WiFi-based indoor line-of-sight identification
title_full_unstemmed WiFi-based indoor line-of-sight identification
title_sort wifi-based indoor line-of-sight identification
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/4536
https://ink.library.smu.edu.sg/context/sis_research/article/5539/viewcontent/twc15_zhou.pdf
_version_ 1770574906494287872