Massive Cooperation for Ultra-Dense Networks

Application details:

Reference number: GZ250717
Start date: 1st April 2018
Closing date: 29th January 2018
Interview date: TBD

Supervisors:

Primary supervisor: Dr. Gan Zheng
Secondary supervisor: Prof. Sangarapillai Lambotharan

In choosing Loughborough for your research, you will 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/

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 tackling 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 guidelines 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.

Informal enquiries about this studentship may be made to Dr. Gan Zheng ([Email Address Removed]).

Find out more:

http://www.lboro.ac.uk/departments/meme/staff/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.

Contact details:

Name: Gan Zheng
Email address: [Email Address Removed]
Telephone number: +44(0)1509227035

How to apply:

All applications should be made online at http://www.lboro.ac.uk/study/apply/research/. Under programme name, select ‘Electronic, Electrical and Systems Engineering’

Please quote reference number: GZ250717