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Dynamic Optimization of AI based 5G networks

  • Full or part time
  • Application Deadline
    Friday, January 31, 2020
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

Wireless communication technologies such as fifth generation mobile networks (5G) will not only provide an increase of 1000 times in the Internet traffic in the next decade but will also offer the underlying technologies to entire industries to support Internet of things (IOT) technologies. Compared to existing mobile communication techniques, 5G has more varied applications and its corresponding system design is more complicated. The resurgence of artificial intelligence (AI) techniques offers an alternative option that is possibly superior to traditional ideas and performance.

This a multi-disciplinary project that aims to develop new hierarchical optimization techniques for emerging AI based 5G cellular networks. In collaboration with experts in communication system theory and implementation at the School of Engineering we aim to develop dynamic multi-objective optimization techniques that address aspects such as Quality of Service, enhanced individual user experience, network capacity, and channel quality on the one hand with aspects such as antenna/receiver complexity, IoT infrastructure, data and energy efficiency on the other.

Entry requirements:


• A Bachelor’s degree in Operational Research, Engineering, Mathematics or similar (a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university);

• Experience with optimization modelling;

• Programming ability in high-level scientific development language, e.g. Python, R, Matlab;

• Strong verbal and written communication skills in English.


• Mathematical maturity;

• Experience with wireless network communication technologies;

• Experience with optimization solvers;

• A Master’s degree with prior publications in optimization and/or wireless networks.

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