Massive Cooperation for Ultra-Dense Networks PhD

Mechanical, Electrical and Manufacturing Engineering
Entry requirements:
3 years
not available
Reference number:
Start date:
01 July 2018
Is funding available?
UK/EU fees:
International fees:
Application deadline:
07 May 2018



in the UK for research quality

REF 2014


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Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%.

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. 

Project detail

Wireless networks need to support 1000 times increase in data traffic by 2020 compared to the 2010

level.  To address this crisis, the ultra-dense network (UDN) has become one of the most promising solutions for their ability to provide remarkable regional capacity. However, the true potential of UDN is much more than just providing localised capacity but it offers a platform that allows massive cooperative signal and data processing to help understand the user requirements, make meaningful predictions and more importantly, take proactive actions to address the anticipated traffic fluctuations.

This PhD project will focus on two complementary studies of UDNs: 1) to design optimization and signal processing techniques to enable massive signal cooperation. This requires to tackle the difficulty of overhead and explore interference to advance signal cooperation. 2) to improve future wireless design by exploring large-scale data cooperation using analytic tools. Specifically, big data will provide guideline for the design of advanced wireless technologies, such as wireless network virtualization, software defined networking, mobile edge computing, Fog-RAN, etc.  The complementary studies in this PhD project will lay the theoretical foundation for delivering, processing and mining wireless big data using UDNs.

The candidate is expected to develop new signal processing algorithms and predictive methods using optimization, game theory as well machine learning and data mining tools to fully explore the massive cooperation opportunities in UDNs.

The candidate will attend conferences and workshops to present their research, and also present to wider audience.


Primary supervisor: Dr. Gan Zheng

Secondary supervisor: Prof. Sangarapillai Lambotharan

Find out more

Informal enquiries about this studentship may be made to Dr. Gan Zheng.

Entry requirements

Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Electrical/Electronic Engineering or a related subject.

A relevant Master’s degree and/or experience in one or more of the following will be an advantage: wireless communications, signal processing and machine learning. The applicant should have strong programming skills in languages such as Matlab, Python, C++, etc. The applicant must have good communication skills, be fluent in English and self-motivated, and be a good team member. Students with machine learning background are strongly advised to apply.

Applicants must meet the minimum English Language requirements, details available on the website.

Fees and funding


Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Special arrangements are made for payments by part-time students.

This project is funded by The Leverhulme Trust. The studentship will start in July 2018, for three years, and currently provides a tax-free stipend of £14,553 per annum plus tuition fees at the UK/EU rate (currently £4,195 p.a.). Due to funding restrictions, this is only available to those who are eligible to pay UK/EU fees.

How to apply

All applications should be made online.  Under programme name, select Electronic, Electrical and Systems Engineering

Please quote reference number: GZ250717

Application details

Reference number:  GZ250717
Application deadline: 07 May 2018
Start date: 01 July 2018
Interview date: TBC