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

#### **Thesis description**

Future wireless systems may evolve beyond isolated devices toward a **Super-UE** : a coordinated cluster of smartphones, AI glasses, smart watches, earbuds, and wearables that **pools their antenna, communication, computing, and sensing capabilities** as **one logical terminal**. Together they **enhance connectivity, enable distributed AI inference, and improve user experience**. Cooperation, however, is **neither 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 therefore _intelligently determining when, with whom, and to what extent devices should cooperate, ensuring that the benefits outweigh the costs._

This thesis targets an **AI-native framework** addressing two coupled challenges. First, compact, progressively **refinable semantic representations** that let heterogeneous devices exchange task-relevant information at capability-matched levels of detail. Second, an uncertainty-aware value-of-cooperation controller learning whether to cooperate, with which devices, and how much information and computation to exchange, priced by probing, communication, processing, and coordination costs, named as the **semantic 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.