Joint Device Positioning and Clock Synchronization in 5G Ultra-Dense Networks

In this paper, we address the prospects and key enabling technologies for highly efficient and accurate device positioning and tracking in fifth generation (5G) radio access networks. Building on the premises of ultra-dense networks as well as on the adoption of multicarrier waveforms and antenna arrays in the access nodes (ANs), we first formulate extended Kalman filter (EKF)-based solutions for computationally efficient joint estimation and tracking of the time of arrival (ToA) and direction of arrival (DoA) of the user nodes (UNs) using uplink reference signals. Then, a second EKF stage is proposed in order to fuse the individual DoA and ToA estimates from one or several ANs into a UN position estimate. Since all the processing takes place at the network side, the computing complexity and energy consumption at the UN side are kept to a minimum. The cascaded EKFs proposed in this article also take into account the unavoidable relative clock offsets between UNs and ANs, such that reliable clock synchronization of the access-link is obtained as a valuable by-product. The proposed cascaded EKF scheme is then revised and extended to more general and challenging scenarios where not only the UNs have clock offsets against the network time, but also the ANs themselves are not mutually synchronized in time. Finally, comprehensive performance evaluations of the proposed solutions on a realistic 5G network setup, building on the METIS project based outdoor Madrid map model together with complete ray tracing based propagation modeling, are provided. The obtained results clearly demonstrate that by using the developed methods, sub-meter scale positioning and tracking accuracy of moving devices is indeed technically feasible in future 5G radio access networks operating at sub-6 GHz frequencies, despite the realistic assumptions related to clock offsets and potentially even under unsynchronized network elements.

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

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

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

[4]  Chan Zhou,et al.  Robustness of Location Based D2D Resource Allocation against Positioning Errors , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[5]  Gerhard P. Fettweis,et al.  The Tactile Internet: Applications and Challenges , 2014, IEEE Vehicular Technology Magazine.

[6]  Visa Koivunen,et al.  Detection and Tracking of MIMO Propagation Path Parameters Using State-Space Approach , 2009, IEEE Transactions on Signal Processing.

[7]  Björn E. Ottersten,et al.  Detection and estimation in sensor arrays using weighted subspace fitting , 1991, IEEE Trans. Signal Process..

[8]  R. Akcelik,et al.  Acceleration Profile Models for Vehicles in Road Traffic , 1987, Transp. Sci..

[9]  Moe Z. Win,et al.  Cooperative Localization in Wireless Networks , 2009, Proceedings of the IEEE.

[10]  Visa Koivunen,et al.  Time Synchronization and Ranging in OFDM Systems Using Time-Reversal , 2013, IEEE Transactions on Instrumentation and Measurement.

[11]  Fernando M. L. Tavares,et al.  5G small cell optimized radio design , 2013, 2013 IEEE Globecom Workshops (GC Wkshps).

[12]  Agus Budiyono,et al.  Principles of GNSS, Inertial, and Multi-sensor Integrated Navigation Systems , 2012 .

[13]  Mikko Valkama,et al.  Radio Interface Evolution Towards 5G and Enhanced Local Area Communications , 2014, IEEE Access.

[14]  Mikko Valkama,et al.  High-Efficiency Device Localization in 5G Ultra-Dense Networks: Prospects and Enabling Technologies , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[15]  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.

[16]  Deborah Estrin,et al.  GPS-less low-cost outdoor localization for very small devices , 2000, IEEE Wirel. Commun..

[17]  Fernando Seco Granja,et al.  Indoor Positioning Using Efficient Map Matching, RSS Measurements, and an Improved Motion Model , 2015, IEEE Transactions on Vehicular Technology.

[18]  Gilberto Berardinelli,et al.  On the TDD subframe structure for beyond 4G radio access network , 2013, 2013 Future Network & Mobile Summit.

[19]  Simo Särkkä,et al.  Bayesian Filtering and Smoothing , 2013, Institute of Mathematical Statistics textbooks.

[20]  Benoît Champagne,et al.  Sensor localization in NLOS environments with anchor uncertainty and unknown clock parameters , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[21]  Mikko Valkama,et al.  Location Based Beamforming in 5G Ultra-Dense Networks , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[22]  Visa Koivunen,et al.  DoA and Polarization Estimation for Arbitrary Array Configurations , 2012, IEEE Transactions on Signal Processing.

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

[24]  T. Subba Rao,et al.  Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB , 2004 .

[25]  Alle-Jan van der Veen,et al.  Joint Ranging and Synchronization for an Anchorless Network of Mobile Nodes , 2014, IEEE Transactions on Signal Processing.

[26]  T. Kailath,et al.  Spatio-temporal spectral analysis by eigenstructure methods , 1984 .

[27]  Mikko Valkama,et al.  Joint User Node Positioning and Clock Offset Estimation in 5G Ultra-Dense Networks , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[28]  Lazaros Gkatzikis,et al.  Beam-searching and transmission scheduling in millimeter wave communications , 2015, 2015 IEEE International Conference on Communications (ICC).

[29]  A. Roxin,et al.  Survey of Wireless Geolocation Techniques , 2007, 2007 IEEE Globecom Workshops.

[30]  K.J.R. Liu,et al.  Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs , 2005, IEEE Signal Processing Magazine.

[31]  C.-C. Jay Kuo,et al.  Multi-Carrier Techniques for Broadband Wireless Communications - A Signal Processing Perspective , 2007, Communications and Signal Processing.

[32]  Tommy Svensson,et al.  Location-Aware Communications for 5G Networks: How location information can improve scalability, latency, and robustness of 5G , 2014, IEEE Signal Processing Magazine.

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

[34]  Junyi Li,et al.  Network densification: the dominant theme for wireless evolution into 5G , 2014, IEEE Communications Magazine.

[35]  Anthony J. Weiss,et al.  Passive Localization and Synchronization Using Arbitrary Signals , 2014, IEEE Transactions on Signal Processing.

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

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

[38]  Mikko Valkama,et al.  Estimating the primary user location and transmit power in cognitive radio systems using extended Kalman filters , 2013, 2013 10th Annual Conference on Wireless On-demand Network Systems and Services (WONS).

[39]  Benoît Champagne,et al.  Mobile Localization in Non-Line-of-Sight Using Constrained Square-Root Unscented Kalman Filter , 2014, IEEE Transactions on Vehicular Technology.

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

[41]  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).

[42]  Arno Solin,et al.  Optimal Filtering with Kalman Filters and Smoothers , 2011 .

[43]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[44]  Taoka Hidekazu,et al.  Scenarios for 5G mobile and wireless communications: the vision of the METIS project , 2014, IEEE Communications Magazine.

[45]  Mònica Navarro,et al.  Frequency Domain Joint TOA and DOA Estimation in IR-UWB , 2011, IEEE Transactions on Wireless Communications.

[46]  José M. F. Moura,et al.  Distributing the Kalman Filter for Large-Scale Systems , 2007, IEEE Transactions on Signal Processing.

[47]  Dario Petri,et al.  Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment , 2011, IEEE Transactions on Instrumentation and Measurement.

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

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

[50]  Kameshwar Poolla,et al.  On Filtering and Smoothing , 1992 .

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

[52]  Jonathan Bosse,et al.  A Spatio-Temporal Array Processing for Passive Localization of Radio Transmitters , 2013, IEEE Transactions on Signal Processing.