Paper Info
Title
Ampn: Real-Time Los/Nlos Identification With Wifi
Abstract
WiFi technology has fostered numerous mobile computing applications, e.g. indoor localization, gesture and activity recognition, device-free localization, etc., due to its ubiquity. The awareness of LOS and NLOS is a prerequisite for WiFi-based methods, since the WiFi signals received under NLOS conditions may contain a lot of noise and multipath effects, exerting great influences on the accuracy of location or identification. Traditional schemes based on commodity WiFi devices can achieve real-time LOS/NLOS identification. However, these methods face the challenges of limited bandwidth and coarse multipath resolution. In this work, we explore the amplitude feature of PHY layer information, and accordingly propose AmpN, a real-time LOS identification scheme based on commodity WiFi infrastructure that is applicable in both static and mobile scenarios. AmpN employs BP neural network algorithm in static scenario and K-Mean method in dynamic scenario, respectively. Experimental results demonstrate that AmpN outperforms existing approaches, achieving overall LOS and NLOS detection rates of 94.2% and 97.6% in static case, and above 97% LOS and NLOS detection rates in mobile context. In addition, the detection delay is less than 0.4s when the link state switches from LOS to NLOS.
Year
Venue
Keywords
2017
2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)
Line-Of-Sight (LOS), Non-Line-Of-Sight (NLOS), BP neural network, Rician factor, K-Mean
Field
DocType
ISSN
Mobile computing,Non-line-of-sight propagation,Multipath propagation,Link-state routing protocol,Identification scheme,Computer science,Computer network,Real-time computing,Bandwidth (signal processing),Physical layer,Mobile telephony
Conference
1550-3607
Citations 
PageRank 
References 
0
0.34
14
Authors
5
Name
Order
Citations
PageRank
Fu Xiao111535.24
Zhengxin Guo211.36
Hai Zhu38722.69
Xiaohui Xie462452.73
Ruchuan Wang541464.49