Developing New Signal Processing Algorithms for Massive Cooperation for Ultra-Dense Networks

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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.

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.

Application details

Reference number: GZ250717
Closing date: 7th May 2018
Start date: 1st July 2018
Interview date: TBD