Master's Thesis: AI-Native Super-UE for 6G

Thesis description

Future wireless systems may evolve beyond isolated devices toward aSuper-UE: a coordinated cluster of smartphones, AI glasses, smart watches, earbuds, and wearables thatpools their antenna, communication, computing, and sensing capabilities asone logical terminal. Together theyenhance connectivity, enable distributed AI inference, and improve user experience. Cooperation, however, isneither free nor always beneficial: partner selection requires measurements and signaling; information exchange consumes radio resources, energy, and synchronization overhead, while capabilities, channels, and application requirements change dynamically. The key challenge is thereforeintelligently determining when, with whom, and to what extent devices should cooperate, ensuring that the benefits outweigh the costs.

This thesis targets anAI-native framework addressingtwo coupled challenges. First,compact, progressivelyrefinable semantic representations that let heterogeneous devices exchange task-relevant informationat capability-matched levels of detail. Second, anuncertainty-aware value-of-cooperation controllerlearning whether to cooperate, with which devices, and how much information and computation to exchange, priced by probing, communication, processing, and coordination costs, named as thesemantic token tax,to jointly optimize the value of exchange against the net benefit of cooperation. Through modeling, learning-based design, and system-level simulations, the thesis will quantify inference accuracy, user experience, communication efficiency, latency, and energy, identify when cooperation is beneficial, and deliver a reproducible framework and proof-of-concept demo.

Qualifications

• Master’s student in Computer Science, Electrical Engineering, Mathematics, or a related field.

• Good knowledge of AI/ML with interest in signal processing and wireless communications.

• Python programming skills, with experience in PyTorch or TensorFlow.

Preferred starting date

ASAP in 2027.