Abstract
In the context of mobile communication systems, user/device location awareness is an essential feature, e.g., for IoT applications. Therefore, it is an integral part of the design of mobile cellular systems (4G LTE, NB-IoT, 5G, and beyond-5G) and, as also highlighted by current investigations in the research community, more efficient and reliable solutions are needed. By leveraging measurements performed on real networks, this thesis work will focus on the development and testing of positioning techniques in cellular network scenarios, potentially applying machine and deep learning techniques.
Keywords
5G system
indoor positioning systems
machine learning
wireless systems
ERC sector(s)
PE Physical Sciences and Engineering
Name supervisor
Giuseppe Caso
E-mail
giuseppe.caso@kau.se
Name of Department/Faculty/School
Department of Mathematics and Computer Science
Name of the host University
Karlstad University (KAU)
EUNICE partner e-mail of destination Research
james.lees@kau.se
Country
Sweden
Thesis level
Master
Minimal language knowledge requisite
English B2
Thesis mode
Hybrid
Start date
Length of the research internship
9 months
Financial support available (other than E+)
Maybe