Channel Estimation using Machine Learning in Next Generation Wireless Networks

Summary

Next generation wireless networks (NGWNs) 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 the device level. It is expected that NGWNs will be extremely efficient in terms of spectral, coverage, energy and RF-EMF exposure [1]. 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 [2]. 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 [3-4]. However, these methods still require 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, frequency bands and efficiency (e.g. computation and controlled RF-EMF exposure) [5].

In this PhD project, we will explore ML-inspired channel estimation in an 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 addition, we will evaluate whether quantum machine learning (QML) is applicable to the problem under discussion and benchmark performance against traditional approaches. The selected candidate will have the opportunity to use state-of-the-art testing and measurement facilities at the Advanced Wireless Technologies Lab (AWTL).

Essential criteria

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.

• A comprehensive and articulate personal statement demonstrating interest in the subject area
• Experience using research methods or other approaches relevant to the subject domain

Desirable Criteria

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

• Applicants should hold, or expect to hold a First Class Honours Degree in a relevant subject area
• International applicants should hold, or expect to hold a relevant Masters Degree at a level equivalent to a distinction at Ulster
• Experience or knowledge of research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Track record of peer reviewed publications

Equal Opportunities

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.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

PhD Scholarships are available for this project and will cover:

• Tuition fees.
• A maintenance allowance of approximately £22,000 (exact rate is to be confirmed) per annum for three years, subject to satisfactory academic performance.
• A Research Training Support Grant (RTSG) of approximately £900 per annum.

To be eligible for these scholarships applicants must: be a UK or Irish national; or have pre-settled or settled status; or have indefinite leave to remain or enter.

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 i.e. September 2027.

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, funded from any source, including self-financing students, are NOT eligible to apply for an award.

Students in full-time employment are not eligible for an award.

Due consideration should be given to financing your studies.

Part-time PhD scholarships may be available at a 0.5 pro rata rate of the full-time scholarship, which would require a six-year registration period.

The University will award a successful candidate one of the following scholarships for the project, depending on the funding available at the time an admissions offer is made:

1. Department for the Economy (DfE) PhD Scholarships
2. Vice Chancellor’s Research Scholarships (VCRS)

Please note that the Terms and Conditions for scholarships are subject to change for 2027/28.