Fakultät für Technische Wissenschaften
The use of drones, a.k.a. unmanned aerial vehicles (UAVs) as a flying radio access network (RAN) is currently gaining significant attention. It holds promises as a complement to classical fixed infrastructure by allowing ultra flexible deployments, with use cases ranging from disaster recovery scenarios to improving the performance and coverage of the network. Beyond obvious challenges within regulatory, control, navigation, and operational domains, the deployment of autonomous flying-RANs also come with a number of exciting new research problems such as the issue of autonomous real-time placement of the drones in non-trivial propagation scenarios (i.e. scenarios where the optimal placement is not just dictated by a trivial geometry or statistical argument due to shadowing effects, e.g. in cities). We present several different approaches, lying at the cross-roads between machine learning, signal processing, and optimization. Some approaches involve the reconstruction of a city map from sampled radio measurements which can have application beyond the realm of communications.
Omid Esrafilian, MSc
Kerstin Smounig (kerstin [dot] smounig [at] aau [dot] at)