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Optimisation and Machine Learning for Massive Ultra-Reliable Low-Latency Communication (URLLC) Networks (WS10)

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  • Full or part time
    Dr M Derakhshani
    Prof S Lambotharan
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Loughborough University is a top-ten rated university in England for research intensity (REF2014). In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career.
Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/

Full Project Detail

We invite applications for a 3-year PhD studentship to study optimization and machine learning algorithms aiming to enable emerging applications for ultra-reliable and low-latency communications, in the Wolfson School of Mechanical, Electrical and Manufacturing Engineering at Loughborough University. The successful applicant will join the Signal Processing and Networks Research Group, under the supervision of Dr. Mahsa Derakhshani.
This project aims to perform fundamental research in telecommunications networks, focusing on developing optimization and machine learning algorithms to guarantee predefined successful packet transmission rate within a stringent prespecified latency budget required for URLLC applications in massive machine-to-machine communication networks. Learning-based algorithms can enable the system to understand the network dynamism, forecast based on the context information, and proactively manage resources to achieve the network-level and user-level performance targets such as Quality-of-Service (QoS) requirements. This project also involves theory building to study the scalability, performance and stability of the learning-based algorithms in large-scale networks with unknowns and imperfections.

Find out more: Applicants seeking additional information are invited to contact Dr Mahsa Derakhshani ([Email Address Removed]).

Entry requirements

Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Electrical Engineering, Computer Science or a related subject. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: MATLAB/Python programming, optimisation techniques, and machine learning algorithms.

Funding Notes

Please note that studentships will be awarded on a competitive basis to applicants who have applied to this project and other advertised projects starting with advert reference ‘WS’ for the School of Mechanical, Electrical and Manufacturing Engineering.
If awarded, each 3-year studentship will provide a tax-free stipend of £15,009 p/a, plus tuition fees at the UK/EU rate (currently £4,327 p/a). While we welcome applications from non-EU nationals, please be advised that it will only be possible to fund the tuition fees at the international rate and no stipend will be available. Successful candidates will be notified by 30th September 2019.



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