We seek to develop appropriate radio frame and reference signal structures, to support high-efficiency positioning, as well as needed signal processing solutions in individual network element, such as direction-of-arrival and time-of-arrival.
We seek to develop data fusion solutions to extract and track the device locations in an "always on" manner as an integral part of 5G networks such that highly accurate device location estimates are available at any given moment without draining the device batteries.
In order to meet the demanding communication requirements of future 5G networks, it is expected that such networks will be deployed with wide bandwidths. Such bandwidths will most likely be located at high frequencies where the free spectrum is more easily available. Due to the high carrier frequency and concequently more demanding propagation environments, it is envisioned that network densification will also play an important role in future 5G networks. Such densely deployed access nodes (ANs) in turn increase the line-of-sight (LoS) probability between the user node (UN) and ANs, thus enabling highly accurate time of arrival (ToA) estimation. It is also expected that ANs in 5G networks will be employed with smart antenna solutions such as antenna arrays which in turn enable also accurate direction of arrival (DoA) estimation. In general, all the aforementioned measurements among others can be efficiently estimated from uplink (UL) plitos signals in a network-centric manner such that additional positioning dedicated signals are not necessary required. In this respect, we seek to develop appropriate radio frame and reference signal structures, to support high-efficiency positioning, as well as needed signal processing solutions in individual network element for DoA and ToA estimation, for instance.
In general, 5G networks are expected to provide a convenient environment for positioning due to large antenna arrays and wide bandwidths which, in turn, enable highly accurate DoA and ToA estimation especially in LoS conditions which will most likely be even more common in future networks due to envisioned network densification. In contrast to the existing radio networks where the positioning has only been an add-on feature, positioning will play a key role in future 5G radio networks. Therefore, our goal is to develop and study different technologies and techniques for highly accurate and energy-efficient positioning that is feasible for most of the foreseen applications and use cases. Based on the achieved results, a network-centric positioning solution is able to provide highly accurate 3D location estimates for different use cases like vehicles and drones in a continuous manner without ruining the batteries at the devices.
Positioning in general will play a tremendeous role in future 5G networks, thus enabling not only a vast amount of different location-based services and applications such as intelligent traffic system (ITS) and autonomous vehicles, but also valuable location-aware communication solutions like proactive radio resource management (RRM), for instance. Therefore, we also develop concepts how the location information can be used in the radio network for enhanced communications and mobility management, or alternatively offered to catalyze and enable appealing third-party location-based services like intelligent transportation systems (ITSs) and self-driving cars.