Summary
During the past decade, with the rapid development and spread of the Internet of Things (IoT), cloud
computing, and intelligent terminals, the application of Location-Based Services (LBS) has attracted
wide attention from both academia and industry. In the outdoor environment, satellite-based positioning
technologies (like GPS) can provide convenient location services for people, to support applications
such as vehicle navigation and cargo tracking. However, in indoor environments and the dense urban
areas, due to severe object occlusion and multipath effects of signal propagation, the accuracy of
satellite-based positioning technologies decreases and cannot meet the applications’ demands.
Accurate and real-time positioning is highly demanded by location-based services and can be beneficial
for radio resource management in 5G networks currently being deployed to achieve significant
performance improvement over existing cellular networks. Many new technologies, for example,
massive Multiple Input Multiple Output (MIMO), millimeter Wave (mmWave) communication, Ultra-
Dense Network (UDN), and Device-to-Device (D2D) communication, are introduced in 5G networks,
not only to enhance communication performance but also to provide the opportunity to improve
positioning accuracy significantly. It is envisioned that 5G networks will be capable of locating a User
Equipment (UE) with an accuracy of sub-meter and with high network utility.
In the year 2020, the research team made several steps forward in outdoor and indoor positioning. A
novel fingerprinting-based mobile positioning method was developed which uses a weighted-likelihood
approach combined with a grid cell filtering method to determine the position based on the radio
network measurements. The novel method can be used in every (indoor and outdoor) environment and
contains various parameters that can be fine-tuned for every environment. After it was tested in various
environments, such as rural, suburban, and dense urban environments in Japan, rural environments in
Germany, and urban environments in the USA, it was clear that it outperforms the legacy AECID
mobile positioning method. The new solution replaced the legacy one at the end of the year. Our novel
method was further improved with measurement clustering and propagation model-based positioning.
These new features are in test phase and show great potential not just in positioning accuracy but in
computational time too. Hopefully, these new features will be deployed in 2021.
Pasic Alija (Mogyorosi Ferenc, Revisnyei Peter)
2021/ 2/ 6.
Támogató: Ericsson
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