  It is commonly expected that future fifth generation (5G) networks will be
deployed with a high spatial density of access nodes (ANs) in order to meet the
envisioned capacity requirements of the upcoming wireless networks.
Densification is beneficial not only for communications but it also creates a
convenient infrastructure for highly accurate user node (UN) positioning.
Despite the fact that positioning will play an important role in future
networks, thus enabling a huge amount of location-based applications and
services, this great opportunity has not been widely explored in the existing
literature. Therefore, this paper proposes an unscented Kalman filter
(UKF)-based method for estimating directions of arrival (DoAs) and times of
arrival (ToA) at ANs as well as performing joint 3D positioning and network
synchronization in a network-centric manner. In addition to the proposed
UKF-based solution, the existing 2D extended Kalman filter (EKF)-based solution
is extended to cover also realistic 3D positioning scenarios. Building on the
premises of 5G ultra-dense networks (UDNs), the performance of both methods is
evaluated and analysed in terms of DoA and ToA estimation as well as
positioning and clock offset estimation accuracy, using the METIS map-based
ray-tracing channel model and 3D trajectories for vehicles and unmanned aerial
vehicles (UAVs) through the Madrid grid. Based on the comprehensive numerical
evaluations, both proposed methods can provide the envisioned one meter 3D
positioning accuracy even in the case of unsynchronized 5G network while
simultaneously tracking the clock offsets of network elements with a
nanosecond-scale accuracy.
