PROJECT TITLE :

WiFi-Based Indoor Line-of-Sight Identification

ABSTRACT:

Wireless LANs, particularly WiFi, are pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in coming up with 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 strong localization. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive thanks to the coarse multipath resolution with MAC-layer received signal strength. During this paper, we tend to propose 2 PHY-layer channel-statistics-primarily based options from each the time and frequency domains. To any break off from the intrinsic bandwidth limit of WiFi, we tend to extend to the spatial domain and harness natural mobility to enlarge the randomness of NLOS ways whereas 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 150zero m two. Experimental results demonstrate that LiFi achieves an overall LOS detection rate of 90.forty two% with a false alarm rate of nine.34% for the temporal feature and an overall LOS detection rate of ninety three.09% with a false alarm rate of seven.29% for the spectral feature.