Joint User Node Positioning and Clock Offset Estimation in 5G Ultra-Dense Networks

It is commonly expected that network densification will play an important role in achieving the capacity demands of 5G communication networks. While densification is introduced to improve the spectral efficiency and area-capacity, it also results in an infrastructure that is perfectly suitable for user node (UN) positioning. However, so far this compelling opportunity has not been clearly recognized in the literature. In this paper, we therefore propose to make "always on" positioning an integral part of 5G networks such that highly accurate user location estimates are available at any given moment but without draining the UN batteries. We furthermore propose an extended Kalman filter (EKF) that tracks the user location based on the fusion of direction of arrival (DoA) and time of arrival (ToA) estimates obtained at the access nodes (ANs) of the 5G network. Since ToA estimates are typically not useful for positioning unless the UN is synchronized with the network, we include a realistic clock model within the DoA/ToA EKF. This addition makes it possible to estimate the offset of the imperfect UN clock, along with the UN position. In an extensive analysis that is based on specific 5G simulation models, we then quantify the enormous potential of high accuracy positioning in 5G networks, in general, and the proposed DoA/ToA EKF, in particular. Moreover, we demonstrate that the proposed DoA/ToA EKF substantially outperforms the classical DoA-only EKF and is furthermore also able to handle practically extremely relevant situations where the DoA-only EKF fails to position the UN.

[1]  Yik-Chung Wu,et al.  Clock Synchronization of Wireless Sensor Networks , 2011, IEEE Signal Processing Magazine.

[2]  D. Simon Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[3]  Yiran Chen,et al.  Demystifying energy usage in smartphones , 2014, 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC).

[4]  Bogdan Groza,et al.  Fingerprinting Smartphones Remotely via ICMP Timestamps , 2013, IEEE Communications Letters.

[5]  Jussi Turkka,et al.  A Novel Radio Frame Structure for 5G Dense Outdoor Radio Access Networks , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[6]  Gaetano Giunta,et al.  Dynamic LOS/NLOS Statistical Discrimination of Wireless Mobile Channels , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[7]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[8]  Jonas Medbo,et al.  Propagation channel impact on LTE positioning accuracy: A study based on real measurements of observed time difference of arrival , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[9]  Jie Yang,et al.  Push the limit of WiFi based localization for smartphones , 2012, Mobicom '12.

[10]  M. Navarro,et al.  TOA and DOA Estimation for Positioning and Tracking in IR-UWB , 2007, 2007 IEEE International Conference on Ultra-Wideband.

[11]  Benjamin R. Hamilton,et al.  Tracking Low-Precision Clocks With Time-Varying Drifts Using Kalman Filtering , 2012, IEEE/ACM Transactions on Networking.

[12]  Petar M. Djuric,et al.  Indoor Tracking: Theory, Methods, and Technologies , 2015, IEEE Transactions on Vehicular Technology.

[13]  V. Aidala Kalman Filter Behavior in Bearings-Only Tracking Applications , 1979, IEEE Transactions on Aerospace and Electronic Systems.

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

[15]  T. Kohno,et al.  Remote physical device fingerprinting , 2005, 2005 IEEE Symposium on Security and Privacy (S&P'05).