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  Artificial Intelligence enabled Distributed Edge-Centric Cellular Networks

   School of Physics, Engineering and Technology

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Today, wireless cellular networks rely heavily on the core network to deliver services. This introduces latency and other inefficiencies. Next generation networks will be much more edge centric, enabling ultra-reliable low latency (URLLC) services to be delivered. This requires a fundamental rethink in how edge-centric cellular networks should be delivered.

This project will specifically examine how reconfigurable distributed multi-antenna radio networks can be used to deliver future 6G services, including URLLC and enhanced mobile broadband. A key approach will be to understand how different edge network infrastructure (including edge caches and computing) and antenna sites, including those on moving nodes, can be dynamically integrated to create pop-up cells on demand, to serve vehicular, airborne, and mobile users.  

Artificial intelligence (AI) will be used to learn appropriate strategies to help organise this complex distributed problem of network organisation, which encompasses network self-organisation, resource management and interference control. Different federated AI approaches that exploit historical and embedded information will be examined, including low complexity reinforcement learning, with heuristic acceleration, and high complexity innovative Deep Learning algorithms. The impact of the amount of control information exchanged between nodes, and the degree of federation to assist with the organisation will be examined. A mixture of simulation and analysis will be used to assess performance. Stochastic Geometry, Game theory and Markov analysis will be particularly important analytical tools.

The project will benefit from the University’s new Institute for Safe Autonomy, which is a £35M investment to establish a UK leading institute in the area, exploiting wide ranging industrial connections and state-of-the art experimental facilities.

Entry requirements:

Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Electronic and Electrical Engineering, Physics, Computer Science, Mathematics or a closely related subject.

How to apply:

Applicants should apply via the University’s online application system at Please read the application guidance first so that you understand the various steps in the application process.

Computer Science (8) Engineering (12)

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website View Website for details about funding opportunities at York.

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