Master Thesis on Mesh Networking for Drone Swarms (f/m/d)

The DLR Institute of Communications and Navigation is dedicated to mission-oriented research in selected areas of communications and navigation. Its work ranges from the theoretical foundations to the demonstration of new procedures and systems in a real environment and is embedded in DLR's Space, Aeronautics, Transport, Security and Digitalization programmes. What to expect The Advanced Information Processing Group aims at applying state-of-the-art theoretical results into real-world applications within information processing systems.

The expertise of the group ranges from quantum error correction to Smart Data Management, exploring cutting-edge communication theories such as semantic communication and Age of Information, pushing the boundaries of data utilization and dissemination. The thesis will focus on the analysis and evaluation of advanced 5G networking protocols to support efficient and reliable communications in drone swarms, considering also the application of ML/RL solutions.

Specifically, the use of DECT NR, world’s first non-cellular radio standard to be formally approved as part of the 5G standards by the ITU, will be investigated. The solution enables decentralized protocols in license-exempt spectrum, and can be a fundamental enabler for advanced drone communications.

Your tasks

Familiarize with the physical and MAC layer of the DECT-NR+ standard Study the application of the solution to drone communications, considering traffic requirements as well as constraints in terms of topology and channel impairments Evaluate by means of simulations, the protocol operations and evaluate its suitability for mesh networking in drone swarms for selected applications. Consider the use of machine learning/reinforcement learning solutions to design semantic networking protocols for drone swarms that communicate using DECT NR+

Your profile

Good knowledge of communication systems and signal processing, with particular emphasis on PHY and MAC level protocols. Knowledge of ML/RL algorithms Programming skills (C/C++) and willingness to learn new tools Previous experience with event-driven network simulations is a plus Ability to work independently and interest in interdisciplinary topics Good knowledge of English language We look forward to getting to know you! If you have any questions about this position (Vacancy-ID 5790 ) please

contact:

Dr. Andrea Munari Tel.: +49 (0) 8153 283639