Machine Learning inspired Channel Estimation in Future Wireless networks

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 are found to be effective in wireless communication, particularly for channel estimation in RIS integrated NOMA communication system. 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 stateof-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 SenComm Research Lab. This PhD is jointly supervised with Prof. Trung Q. Duong (Fellow of IEEE).

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.

• Sound understanding of subject area as evidenced by a comprehensive research proposal

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 65%
• Work experience relevant to the proposed project
• Publications - peer-reviewed

The University offers the following levels of support:

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

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.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• 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. Further information on cost of living

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.

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.