TY - GEN
T1 - LiFi
T2 - 33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014
AU - Zhou, Zimu
AU - Yang, Zheng
AU - Wu, Chenshu
AU - Sun, Wei
AU - Liu, Yunhao
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2014
Y1 - 2014
N2 - Wireless LANs, especially WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing each scenario-tailored application is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to distinguish Line-Of-Sight (LOS) path from NLOS paths acts as a key enabler for adaptive communication, cognitive radios, robust localization, etc. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with mere MAC layer RSSI. In this work, we dive into the PHY layer and strive to eliminate irrelevant noise and NLOS paths with long delays from the multipath channel responses. 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 prototype LiFi, a statistical LOS identification scheme for commodity WiFi infrastructure and evaluate it in typical indoor environments covering an area of 1500m2. Experimental results demonstrate an overall LOS identification rate of 90.4% with a false alarm rate of 9.3%. © 2014 IEEE.
AB - Wireless LANs, especially WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing each scenario-tailored application is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to distinguish Line-Of-Sight (LOS) path from NLOS paths acts as a key enabler for adaptive communication, cognitive radios, robust localization, etc. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with mere MAC layer RSSI. In this work, we dive into the PHY layer and strive to eliminate irrelevant noise and NLOS paths with long delays from the multipath channel responses. 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 prototype LiFi, a statistical LOS identification scheme for commodity WiFi infrastructure and evaluate it in typical indoor environments covering an area of 1500m2. Experimental results demonstrate an overall LOS identification rate of 90.4% with a false alarm rate of 9.3%. © 2014 IEEE.
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U2 - 10.1109/INFOCOM.2014.6848217
DO - 10.1109/INFOCOM.2014.6848217
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781479933600
T3 - Proceedings - IEEE INFOCOM
SP - 2688
EP - 2696
BT - IEEE INFOCOM 2014 - IEEE Conference on Computer Communications
PB - IEEE
Y2 - 27 April 2014 through 2 May 2014
ER -