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Learning-to-optimization for network slicing in 5G mobile networks. Computer Science PhD.

  • Full or part time
  • Application Deadline
    Monday, May 13, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The University of Exeter EPSRC DTP (Engineering and Physical Sciences Research Council Doctoral Training Partnership) is offering up to 4 fully funded doctoral studentships for 2019/20 entry. Students will be given sector-leading training and development with outstanding facilities and resources. Studentships will be awarded to outstanding applicants, the distribution will be overseen by the University’s EPSRC Strategy Group in partnership with the Doctoral College.

Supervisors:
Dr Yulei Wu, Department of Computer Science, College of Engineering, Mathematics and Physical Sciences
Prof Geyong Min, Department of Computer Science, College of Engineering, Mathematics and Physical Sciences

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.

Funding Notes

For successful eligible applicants the studentship comprises:

An index-linked stipend for up to 3.5 years full time (currently £14,777 per annum for 2018/19), pro-rata for part-time students.
Payment of University tuition fees (UK/EU)
Research Training Support Grant (RTSG) of £5,000 over 3.5 years, or pro-rata for part-time students

Related Subjects

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