DC11

This research aims to explore unsourced random access (uRA) within a user-centric cell-free massive MIMO (CF-mMIMO) network, developing novel distributed sparse estimation techniques for fast random access, channel estimation, RF sensing. It will also characterise the achievable rates for short-block communication by analysing the trade-off between rate and probability of error using a finite block length framework. The study further investigates fairness scheduling in scenarios where the number of users significantly exceeds the spatial multiplexing gain of the system. Additionally, it focuses on optimising edge computing resource allocation by strategically placing user-centric cluster processors as software-defined network functions hosted in the distributed units (DUs) and optimising data routing through the fronthaul to minimise network load. This research is at the forefront of 6G wireless technology and involves advanced mathematical optimization, information theoretic and coding theoretic concepts, and possibly applications of deep learning to the physical layer.

1. The candidate must not have been awarded a PhD Degree.
2. A Master’s Degree is required in Electrical Engineering, Computer Science, Telecommunications, Applied Mathematics, or a related field.
3. Candidates must not have resided in Germany for more than 12 months within the three years preceding their employment start date. Additionally, for the remainder of this period, candidates must provide evidence of residing outside Germany.
4. Proficiency in written and spoken English is required and must be formally demonstrated.
5. A solid background in statistical signal processing, mathematical optimization, wireless communications (modeling, simulation, general physical layer communication theoretic approaches) is highly desirable. A master-level background in information theory and channel coding is a plus.