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Cognition Assisted 5G Small Cell Networks

Department of Electronic Engineering

About the Project

This project will investigate how machine learning based artificial intelligence strategies can be used to control both the radio resources and network topology in small cell 5G networks, likely to be used in dense urban areas, when traffic is dominated by ultra-high definition video from vehicular users. It is expected that base stations and users will have multiple (smart) antennas, enabling rapid adjustment in the coverage and capacity density. Wireless backhaul/fronthaul is expected in such networks, and the project will examine how multi-hop mm-wave techniques can be exploited. Resource assignment strategies will be developed which deliver the required capacity with low latency. 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.

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, Music Technology 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.

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 Electronic Engineering website View Website for details about funding opportunities at York.

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