Measurement and analysis of NLOS identification metrics for WLAN systems

Indoor localization has received significant attention over the last decade. For WLANs, Received Signal Strength (RSS) based techniques have been the most popular due to the simplicity of measuring the power and the ubiquity of WiFi infrastructure and devices in different indoor environments. Recently, Time-of-arrival (TOA) techniques has been proposed to be incorporated into IEEE 802.11 standards that promise accurate range estimation via hardware time-stamping. Non-line-of sight (NLOS) propagation is a major challenge and many mitigation algorithms have been proposed but their performance is dependent on the accuracy of NLOS identification. By analyzing the moments (e.g. RMS delay, kurtosis) of the time-domain channel impulse response (CIR), it is possible to infer the condition of the channel. Although these metrics have been verified experimentally for ultra-wideband (UWB) systems, their performance for lower-system bandwidths (e.g. WLAN) has not been addressed in the literature. In this paper, using frequency domain channel measurements in a typical indoor office environment, the accuracy of NLOS identification metrics for WLAN systems is analyzed. It is shown that the accuracy of the RMS delay spread and kurtosis is significantly diminished due to low-time resolution of the CIR. Furthermore, it is also shown that by estimating the coherence bandwidth from the channel transfer function, a better NLOS identification metric is obtained in OFDM-based systems. To the best of the authors' knowledge, this experimental analysis to evaluate NLOS identification metrics has never been reported before.

[1]  Elena Simona Lohan,et al.  Analysis of Kurtosis-Based LOS/NLOS Identification Using Indoor MIMO Channel Measurement , 2013, IEEE Transactions on Vehicular Technology.

[2]  Andreas F. Molisch,et al.  Wireless Communications , 2005 .

[3]  Ramjee Prasad,et al.  OFDM for Wireless Communications Systems , 2004 .

[4]  W. Marsden I and J , 2012 .

[5]  Israel Martín-Escalona,et al.  Comparative performance evaluation of IEEE 802.11v for positioning with time of arrival , 2011, Comput. Stand. Interfaces.

[6]  Ismail Güvenç,et al.  NLOS Identification and Mitigation for UWB Localization Systems , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[7]  Ismail Güvenç,et al.  A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques , 2009, IEEE Communications Surveys & Tutorials.

[8]  James Aweya,et al.  Entropy-based non-line of sight identification for wireless positioning systems , 2014, 2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS).

[9]  R. M. Buehrer,et al.  Non-line-of-sight identification in ultra-wideband systems based on received signal statistics , 2007 .

[10]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  James Aweya,et al.  Measurements and characterizations of spatial and temporal TOA based ranging for indoor WLAN channels , 2013, 2013, 7th International Conference on Signal Processing and Communication Systems (ICSPCS).

[12]  Juha-Pekka Makela,et al.  Indoor geolocation science and technology , 2002, IEEE Commun. Mag..

[13]  Mark A Beach,et al.  A comparison of RMS delay spread and coherence bandwidth for characterisation of wideband channels , 1996 .