Learning-to-Optimization for Network Slicing in 5G Mobile Networks - Computer Science - EPSRC DTP funded PhD Studentship Ref: 2894

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Dr Yulei Wu
Professor Geyong Min

Streatham Campus, Exeter

Project Description

To support the rapidly expanding connectivity needs for the next decade and beyond, 5G is envisioned to be a unifying connectivity fabric that will connect virtually everything around us — from enabling enhanced mobile broadband services and mission-critical communications to connecting the massive Internet of Things. The new technology evolution as well as business needs require the 5G to be designed with new levels of flexibility and scalability that will fuel mobile inventions. Key technologies are virtualization, Software Defined Networks (SDN), orchestration capabilities, advances in air interface along with a general evolution of software technologies. With these technologies at hand, it becomes possible to create and operate logical 5G network slices in a dynamic “on-demand” basis, targeting at the customised needs of specific customers, services or business segments. The research and development of 5G network slicing are still in its infancy, thus more work is urgently to be done to implement a much higher degree of automation, orchestration and advanced service creation capabilities.

In this project, we will investigate two emerging open problems facing networking system and protocol design: 1) How to design a scalable end-to-end 5G network slicing architecture to enable the envisioned advanced automation, orchestration and service creation capabilities? 2) How to effectively optimize the 5G network slices to meet the diverse performance, functional and operational requirements? To answer these problems, this project will propose novel learning based 5G network slicing architectures and optimizing solutions for efficient slicing service provisioning in 5G.
In outline, the proposed project intends to:

* Design a scalable end-to-end 5G network slicing architecture to enable the envisioned advanced automation, orchestration and service creation capabilities under a complete investigation of the emerging business requirements and networking challenges. Technologies on intelligent learning, virtualization, Software Defined Networks (SDN), orchestration capabilities, advances in air interface along with a general evolution of software technologies will be jointly considered to enrich the capabilities of the proposed architecture.
* Develop an innovative learning-based resource and service management scheme to optimize the resource utilities and service performance for the network slicing system. Both advanced learning algorithms and classic convex optimization algorithms will be combined to achieve desired optimization outcomes for different complicated scenarios.
* Develop a prototype to validate the proposed network slicing architecture and optimizing models with diverse emerging 5G vertical applications.

Candidates who have background on or who are interested in machine learning, optimisation theory, and 5G networks are suitable for this research.

Entry Requirements
You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in Computer Science. Experience in Machine Learning, Optimisation Theory, and Computer Networks is desirable.

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.


Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral Collegepgrenquiries@exeter.ac.uk

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim
        transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.  For further details of the University’s English language requirements please see

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

If you have any general enquiries about the application process please email: pgrenquiries@exeter.ac.uk.
Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.