In this article, 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 (UDNs) 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 (UL) reference signals. Then, a second EKF stage is proposed in order to fuse the individual DoA/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.

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Using collaborative sensors or other observing devices equipped with sectorized antennas provides a practical and low-cost solution to direction of arrival (DoA) and received signal strength (RSS) estimation, as well as non-cooperative transmitter localization. In this paper, we study the performance and theoretical bounds of DoA/RSS estimation and localization using sectorized antennas. We first show that the sector-power measurements at an individual sensor form a sufficient statistic for DoA/RSS estimation and transmitter localization. Motivated by that, we then derive the Cramer-Rao bound (CRB) on DoA/RSS estimation based on sector-powers and study its asymptotic behavior. Moreover, we derive an analytical expression for the mean squared error of a practical sectorized-antenna based DoA estimator, compare its performance to the derived CRB and study its asymptotic properties. Next, we derive the CRB for localization based on sector-powers. The resulting CRB is a lower bound for a localization system where the DoA/RSS estimates, obtained from sector-powers at individual sensors, are fused together into a location estimate. Moreover, the CRB also covers the more general case of a localization system where sector-powers from individual nodes are directly fused together, without an intermediate DoA/RSS estimation step. We compare the obtained CRB to a localization approach employing an intermediate DoA/RSS estimation step, and observe that skipping this intermediate processing step may result in a substantially improved localization performance. Finally, we study the influence of various important system parameters, like the number of sensors, sectors and measurement samples, on the achievable estimation and localization performance. Overall, this paper demonstrates and quantifies the achievable DoA/RSS estimation and localization performance of sectorized antennas, and provides comprehensive design guidelines for sector-power based low-complexity localization systems. interests include novel radio architecture, signal processing, and networking techniques to implement spectrum sensing functionality in cognitive radios.

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