This project will investigate machine learning enabled security solutions in a programmable radio environment using intelligent reflecting surfaces for next generation wireless communication systems.
It is anticipated that machine learning and artificial intelligence (AI) will become dominant technologies in 6G wireless communication systems. In particular, deep learning will play a significant role in distributed optimization for ultra-dense wireless networks in which classical centralized and distributed optimization approaches can no longer cope with the scale and heterogeneity of the networks. However, the deep learning framework itself needs to be designed in a way that it can handle the distributed nature and heterogeneity of 6G network infrastructure.
With over 50 billion devices connected everywhere via IoT with different levels of capability, 6G will need a holistic approach to secure the sheer volume of mobile data across a diverse set of platforms and comply with the strict latency as well as security requirements. Machine learning and AI will guide new security solutions by predicting behavioural models of suspicious activities of malicious nodes. Deep learning techniques will also facilitate cross-layer security design, localize potential eavesdroppers as well as make physical layer security more practical with real-time channel data.
On the other hand, intelligent reflecting surface (IRS) is a state-of-the-art technology that is able to revolutionize next generation wireless communication systems by smartly reconfiguring the wireless propagation environment with the use of ultra-low-cost passive reflecting elements integrated on a planar surface. More specifically, different elements of an IRS can independently reflect the incident signals by controlling its amplitude and/or phase and thereby collaboratively achieve fine-grained three-dimensional (3D) passive beamforming for directional signal enhancement or nulling. In this project, we envision to exploit machine learning techniques for developing truly intelligent surfaces for secure communications in a challenging environment.
All applicants must have or expect to have a 1st class MChem, MPhys, MSci, MEng or equivalent degree by Autumn 2020. Selection will be based on academic excellence and research potential, and all short-listed applicants will be interviewed (in person or by Skype). This scholarship is only open to UK/EU applicants who meet residency requirements set out by EPSRC.
All applications must be received by 28th February 2020. All successful candidates should usually expect to start in September/October 2020.
How to Apply
Apply Online - https://hwacuk.elluciancrmrecruit.com/Admissions/Pages/Login.aspx
When applying through the Heriot-Watt on-line system please ensure you provide the following information:
(a) in ‘Study Option’
You will need to select ‘Edinburgh’ and ‘Postgraduate Research’. ‘Programme’ presents you with a drop-down menu. Choose Electrical Engineering PhD and select September 2020 for study option (this can be updated at a later date if required)
(b) in ‘Research Project Information’
You will be provided with a free text box for details of your research project. Enter Title and Reference number of the project for which you are applying and also enter the supervisor’s name.
This information will greatly assist us in tracking your application.
Please note that once you have submitted your application, it will not be considered until you have uploaded your CV and transcripts.