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Machine learning and advance signal processing for beyond 5G network automation

Project Description

Network automation has been investigated since LTE due to its significant impact on reduction of operators’ operational expenditure (OPEX). Different features of network automation have been discussed and studied over the years; however, with recent advances in machine learning standardization bodies are considering a more structured use of learning technique in network automation. In this project we will focus on analytics-powered network management approaches to enhance network automation. The project will be aligned with considered features in 3GPP release 16 and 17. The successful candidate will investigate the effects of different learning and signal processing approaches on performance of automated networks with different objectives and primary applications. This includes considering localized automation as of “cognitive self-management”, and network level automation for policy-based management.

Entry requirements:
Candidates must have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Electronic and Electrical Engineering, Physics, Computer Science, Mathematics or a related subject.

How to apply:
Applicants must apply via the University’s online application system at Please read the application guidance first so that you understand the various steps in the application process. To apply, please select the PhD in Electronic Engineering for October 2020 entry. Please specify in your PhD application that you would like to be considered for this studentship.

Funding Notes

This studentship will cover the tuition fee at the home/EU rate (£4,327 in 2019/20) and a stipend at the standard research council rate for a period of up to 3 years (£15,009 in 2019/20). International (non-EU) candidates are also welcome to apply but will be required to pay the difference between the UK/EU and international tuition fee rates (approximately £17,000 per year for the duration of the programme).

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