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Social-aware mobile system optimization based on machine learning

  • Full or part time
  • Application Deadline
    Tuesday, September 10, 2019
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Future mobile systems will not only offer data throughput hundreds of times higher than 4G/5G systems can provide, but will also demand strict data security. This requires radical new techniques which will better integrate the needs of the end-user into the system design.

In this PhD research, we will be investigating emerging research topic to exploit users’ social trust information in forming secure and robust mobile networks. Including the social trust makes the networks design much more complicated, which would again make traditional approaches either difficult to find the optimum scheme or complicated to implement.

To progress, we will investigate machine learning methods particularly the reinforcement learning to obtain new data transmission schemes to satisfy users’ QoS requirements in trust-aware networks.

Entry requirements

Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in electronic engineering, computer engineering, computer science or related areas.

A relevant Master's degree and / or experience in one or more of the following will be an advantage:
electronic engineering, computer engineering, computer science

All students must also meet the minimum English Language requirements:

How to apply

All applications are made online, please select the school/department name under the programme name section and include the quote reference number YGUF2019 -

Funding Notes

This is an open call for candidates who are sponsored or who have their own funding. If you do not have funding, you may still apply, however Institutional funding is not guaranteed. Outstanding candidates (UK/EU/International) without funding will be considered for funding opportunities which may become available in the School.

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