Localization in 5G Networks (with Sapienza University of Rome)
As wireless networks are evolving into 5G, we are able to realise services that requires latencies under one millisecond, data rates of up to ten gigabits per second, extremely high network reliability, and better accuracy in positioning. With location awareness becoming an essential feature of many new markets, positioning is consequently considered as an integral part of the system design of upcoming 5G mobile networks.
Many of these use cases require or benefit from location information, making positioning a key dimension for 5G, especially for IoT type of applications. However, the widely used global navigation satellite system (GNSS) may not be suitable in some cases due to the extra power consumption and costs needed to support GNSS chips. Alternatives enabling reliable localization via direct exploitation of 5G signals are thus needed, as highlighted by current investigations in the research community.
This thesis work focuses on the development and testing of positioning techniques for 5G, leveraging the application of enhanced machine and deep learning techniques on empirical measurements.
Required Skills: Wireless communications fundamentals, Machine Learning fundamentals, Matlab and/or Python programming, knowledge of data processing techniques.