Social-aware mobile system optimization based on machine learning

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 network's 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.

Supervisors

Primary supervisor: Dr Alex Gong

Entry requirements for United Kingdom

Applicants should have, or expect to achieve, at least a 2:1 honours degree (or equivalent international qualification) 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 or computer science.

English language requirements

Applicants must meet the minimum English language requirements. Further details are available on the International website.

Find out more about research degree funding

How to apply

All applications should be made online. Under programme name, select ‘Electronic and Electrical Engineering’. Please quote reference number: UF-YG-2022-2

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