What to expect:

The Swarm Exploration Group develops innovative technologies for resilient communication, navigation, and cooperative autonomy across air, road, and maritime sectors. Its core focus is on integrating communication with precise localization and building prototype systems to enable reliable connectivity and situational awareness in safety-critical and infrastructure-limited scenarios.

The thesis focuses on the design and evaluation of physical layer (PHY) techniques for UAV swarm networks. While standard communications focus on bit-error-rate, the project explores a goal-oriented approach: extracting only the information that is actually relevant for swarm operations (e.g., for localization, navigation, communication) directly from the radio signal. This semantic, data-reduced representation can significantly cut the communication overhead needed for continuous swarm coordination. The work complements higher-layer protocol development (e.g., MAC/DECT NR+) by defining the PHY-layer constraints and data formats required for reliable cooperative operation.

Your tasks:

• Study how to extract relevant, task-specific information (i.e., features) from wireless signals, rather than transmitting raw data, to reduce payload size while preserving the semantic information required for cooperative localization and tracking.
• Evaluate the approach through software simulations, analyzing the impact of channel effects (e.g., multipath, Doppler) on the accuracy of the extracted information necessary for the upper layers to operate effectively.
• Gain hands-on experience testing your methods on real hardware platforms (e.g., SDR, UWB modules, flying drones), implementing the signal processing pipeline for real-time operation in airborne environments.
• Collaborate with the team working on higher-layer protocols to define the best trade-off between raw and processed data transmission to maximize swarm efficiency.

Your profile: 

• Solid background in communication systems, signal processing, and wireless networks.
• Interest in AI-based signal processing (feature extraction, classification) and semantic communications.
• Experience with SDR platforms, radio hardware, and embedded systems (testing software on hardware) is a plus.
• Programming skills in C/C++, MATLAB, and Python.
• Ability to work independently and interest in interdisciplinary research topics (PHY protocols, AI, and hardware).