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

Joint 3D Positioning and Network Synchronization in 5G Ultra-Dense Networks Using UKF and EKF