Public defence in Networking Technology, M.Sc. Hamed Hellaoui
M.Sc. Hamed Hellaoui will defend the thesis "Enhancing the Performance of UAV Communications in Cellular Networks" on 4 November 2022 at 12 (EET) in Aalto University School of Electrical Engineering, Department of Communications and Networking, in lecture hall AS1, Maarintie 8, Espoo.
Opponent: Prof. Ari Pouttu, University of Oulu, Finland
Custos: Prof. Jukka Manner, Aalto University School of Electrical Engineering, Department of Communications and Networking
Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/
Doctoral theses in the School of Electrical Engineering: https://aaltodoc.aalto.fi/handle/123456789/53
Public defence announcement:
The use of cellular networks (4G/5G) as a communication infrastructure for drones has become the current trend. This would mainly enable beyond visual line-of-sight applications and allow drones to benefit from the latest evolutions achieved in cellular networks. Despite the advantages that cellular networks can bring to drones, several issues still need to be addressed. Indeed, cellular networks are deployed to serve ground user equipment (UEs), whereas drones' aerial communications are characterized by different channel conditions. Field evaluations have shown that flying drones can experience poor link quality, or even negatively affect ground communication. In addition, drone applications can be deployed in a challenging environment characterized by different types of QoS (Quality of Services). Furthermore, the consideration of cellular networks for drones can bring more opportunities that merit to be explored to enhance the communications, mainly in terms of taking advantage of the presence of several drones and Mobile Network Operators (MNOs).
The main objective of the dissertation is to contribute to enhancing the performance of drone communications in cellular networks. The contributions of this dissertation can be divided into six categories. First, as aerial communication presents different channel conditions, we are interested in modeling drone communications in cellular networks and deriving expressions that define the performance indicators. All the contributions build from these expressions, and target performing network optimization to enhance drone communications in cellular networks. Second, we consider a cellular network deployed to serve both UEs and drones, and we investigate their co-existence by enhancing their underlying performances. Next, we focus on supporting the co-existence of several QoS types in drone communications. In the fourth and the fifth categories, we focus on new opportunities that cellular networks can bring to drone communications. In particular, we investigate the possibility of taking advantage of the presence of several drones and MNOs in a way to enhance the performance of drone communications. Finally, we explore the use of machine learning in order to enable fast optimization and enhance the performance of drone communications. All the contributions of this dissertation have been validated with a series of performance evaluations.