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Deep learning enabled resource allocation for future wireless networks

  • Full or part time
  • Application Deadline
    Monday, January 28, 2019
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Application details

Reference number: WS05
Start date of studentship: 1st October 2019
Closing date of advert: 28th January 2019
Interview date: TBC


Primary supervisor: Dr. Gan Zheng
Secondary supervisor: Dr. Alex Gong
Short Introductory Paragraph

Loughborough University

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.
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Full Project Detail

Future wireless networks need to address the challenge of fast and dynamic resource allocation to satisfy the demand of massive connections, ultra-low latency and ultra-high high reliability and capacity in mobile networks, autonomous vehicles, drones, and internet-of-things (IoT) networks. However, existing resource allocation algorithms are too complex and slow, and do not fit for purpose. This project will develop a purpose-built deep learning architecture and new algorithms for 1) real-time resource allocation such as optimisation of power, subcarrier bandwidth and beamforming; 2) tackling uncertainties in the radio environment including channel variation, dynamic spectrum sharing, network congestion, user mobility and activities. These algorithms will enable the wireless networks to have the necessary cognition and intelligence to rapidly adapt to the dynamic environment and user demand. Through working on this project and joining our renowned Signal Processing and Networks Research Group, you will have the opportunity to work with world-leading experts in signal processing and communications, and contribute to the innovation of future wireless networks.

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: deep learning, image processing and natural language processing.

Contact details

Name: Dr. Gan Zheng
Email address:
Telephone number: 01509 227035

How to apply

All applications should be made online at Under programme name, select ‘Electronic and Electrical Engineering’

Please quote reference number: WS05

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 tuition fees at the UK/EU rate and a tax-free stipend at the UK Research and Innovation rate. The UKRI stipend value for 2019/20 has not yet been announced but the value for 2018/19 was £14,777. 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 26th March 2019.

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