2016/08/12 by Mike Koivisto, Koivisto, Mike, Mário Costa +7
Engineering · #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Information Theory (cs.IT) #Millimeter-Wave Propagation and Modeling #Power Line Communications and Noise
paper · pdf · doi:10.48550/arxiv.1608.03710
openalex publication_date 2016/08/12 · openalex created_date 2022/08/14 · openalex updated_date 2026/07/28
It is commonly expected that future fifth generation (5G) networks will be\ndeployed with a high spatial density of access nodes (ANs) in order to meet the\nenvisioned capacity requirements of the upcoming wireless networks.\nDensification is beneficial not only for communications but it also creates a\nconvenient infrastructure for highly accurate user node (UN) positioning.\nDespite the fact that positioning will play an important role in future\nnetworks, thus enabling a huge amount of location-based applications and\nservices, this great opportunity has not been widely explored in the existing\nliterature. Therefore, this paper proposes an unscented Kalman filter\n(UKF)-based method for estimating directions of arrival (DoAs) and times of\narrival (ToA) at ANs as well as performing joint 3D positioning and network\nsynchronization in a network-centric manner. In addition to the proposed\nUKF-based solution, the existing 2D extended Kalman filter (EKF)-based solution\nis extended to cover also realistic 3D positioning scenarios. Building on the\npremises of 5G ultra-dense networks (UDNs), the performance of both methods is\nevaluated and analysed in terms of DoA and ToA estimation as well as\npositioning and clock offset estimation accuracy, using the METIS map-based\nray-tracing channel model and 3D trajectories for vehicles and unmanned aerial\nvehicles (UAVs) through the Madrid grid. Based on the comprehensive numerical\nevaluations, both proposed methods can provide the envisioned one meter 3D\npositioning accuracy even in the case of unsynchronized 5G network while\nsimultaneously tracking the clock offsets of network elements with a\nnanosecond-scale accuracy.\n