3D Positioning and Tracking in 5G Networks with Kalman Filtering
Rastorgueva-Foi, Elizaveta (2019)
2019
Sähkötekniikan DI-ohjelma - Degree Programme in Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2019-11-27
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-201911246227
https://urn.fi/URN:NBN:fi:tuni-201911246227
Tiivistelmä
The emerging 5G mobile network will become a unique infrastructure that encompasses the cutting-edge technology with visionary applications. Unprecedented data rates, latency and capacity are going to be a game-changer for a wide range of industries. These new capabilities also promise to overturn everyday life routines of normal people: autonomous traffic and fully automated manufacturing/farming, tele-medicine and remote presence, extended sensor networks and augmented reality - all these novelties are going to turn the world into something only science fiction could imagine.
The ground-breaking advances come at a cost. 5G is shifting the communications into the millimeter-wave (mmW) range which has never been used for this purpose before. MmW links must employ small cells and utilize highly directional antennas in order to counteract high path-loss at these frequencies. In addition to that, the dynamic scenarios imply that the 5G base stations (BSs) are going to serve users with fast and complex mobility, which is a challenge for a system with beamforming. The answers to these problem are multi-connectivity and location-aware communication. The 5G network requires embedded positioning system that can be used independently of other positioning techniques, create little overheads and ideally provide added-value positioning services to other players on the market.
We propose a positioning method that uses network's own reference signals (RSs) to provide accurate positioning and tracking of the mobile users. Our method utilizes multi-connectivity and beamforming in order to estimate direction of departure (DoD) of the RSs from all connected BSs, and then converts the DoD angle estimates into positions. Moreover, most of the computational load is shifted from the user to the BSs and the core network, which helps to save user's battery. This stand-alone positioning method shows a potential to provide accuracy that covers needs for most mobile positioning applications envisioned for the 5G wireless networks.
The ground-breaking advances come at a cost. 5G is shifting the communications into the millimeter-wave (mmW) range which has never been used for this purpose before. MmW links must employ small cells and utilize highly directional antennas in order to counteract high path-loss at these frequencies. In addition to that, the dynamic scenarios imply that the 5G base stations (BSs) are going to serve users with fast and complex mobility, which is a challenge for a system with beamforming. The answers to these problem are multi-connectivity and location-aware communication. The 5G network requires embedded positioning system that can be used independently of other positioning techniques, create little overheads and ideally provide added-value positioning services to other players on the market.
We propose a positioning method that uses network's own reference signals (RSs) to provide accurate positioning and tracking of the mobile users. Our method utilizes multi-connectivity and beamforming in order to estimate direction of departure (DoD) of the RSs from all connected BSs, and then converts the DoD angle estimates into positions. Moreover, most of the computational load is shifted from the user to the BSs and the core network, which helps to save user's battery. This stand-alone positioning method shows a potential to provide accuracy that covers needs for most mobile positioning applications envisioned for the 5G wireless networks.