This project is funded by:
Future wireless networks (FWNs) are expected to accommodate trillions of devices with new diverse use cases and require a new ecosystem to support massive connections, ultra-reliability and intelligence from the network to at device level. It is expected that FWNs will be extremely efficient in terms of spectral, coverage, energy and RF-EMF exposure. In addition, such networks will benefit from integrated space-air-ground-sea networks to accommodate diverse use cases and requirements.
The reconfigurable intelligent surface (RIS) integrated with multiple access approaches such as non-orthogonal multiple access (NOMA) is emerging as a potential solution to control wireless channel characteristics and realise reliable massive connections with desired spectral efficiency. However, one of the critical challenges for the success of such a solution depends on a better understanding of wireless channel characteristics and how this information can be used further for overall performance improvement.
In recent years, machine learning (ML) methods have been found to be effective in wireless communication, particularly for channel estimation in RIS integrated NOMA communication systems. However, still required further investigation to benchmark ML models not only in terms of performance but also considering their scalability with system parameters, generalisation to new wireless environments and efficiency (e.g. computation).
In this PhD project, we will explore ML-inspired channel estimation in RIS integrated NOMA communication system. We will explore state-of-the-art ML methods and benchmark them. We will investigate how ML models can be adapted to both scalability and generalisation scenarios. In the end, we will evaluate if Quantum ML is applicable to problem in discussion. The selected candidate will have the opportunity to use a new state-of-the-art mmWave testing and measurement facility, High Performance Computing facility and will have opportunities to collaborate with wider network via the Advanced Wireless Technologies Lab (AWTL). This PhD is jointly supervised with Prof. Trung Q. Duong (Memorial University of Newfoundland, Canada).
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.
Appointment will be made on merit.
This project is funded by:
These scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance). A Research Training Support Grant (RTSG) of £900 per annum is also available.
To be eligible for these scholarships, applicants must meet the following criteria:
Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Due consideration should be given to financing your studies.
C. Nguyen, T. M. Hoang and A. A. Cheema, "Channel Estimation Using CNN-LSTM in RIS-NOMA Assisted 6G Network," in IEEE Transactions on Machine Learning in Communications and Networking, vol. 1, pp. 43-60, 2023, doi: 10.1109/TMLCN.2023.3278232.
N. Q. T. Thoong, A. A, Cheema, S. R. Khosravirad, O. A. Dobre, and T. Q. Duong, “Channel estimation for reconfigurable intelligent surface-aided 6G NOMA systems using CNN-based quantum LSTM model,” in Proc. IEEE VTC Fall, 2024, Washington DC, USA, pp. 1-5.
T. Q. Duong, J. A. Ansere, B. Narottama, V. Sharma, O. A. Dobre and H. Shin, "Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions," in IEEE Open Journal of Vehicular Technology, vol. 3, pp. 375-387, 2022, doi: 10.1109/OJVT.2022.3202876.
Submission deadline
Monday 24 February 2025
04:00PM
Interview Date
March 2025
Preferred student start date
15th September 2025
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