****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.
