The University of Manchester
Department of Electronic and Electrical Engineering
Supervisors: Dr Zahra Mobini; Prof Daniel Ka Chun So
Application deadline: Monday, January 11, 2027
Funding: Competition Funded PhD Project (Students Worldwide)
About the Project
Sixth-generation (6G) wireless networks are expected to provide high data rates, seamless connectivity and reliable services in increasingly dynamic environments. Cell-free massive multiple-input multiple-output (MIMO) has emerged as a promising architecture in which geographically distributed access points (APs) cooperatively serve users without conventional cell boundaries. However, the performance of these networks depends on their ability to adapt to changing propagation conditions, user mobility, traffic demands and network disruptions.
This PhD project aims to develop novel mathematical models, optimisation algorithms and artificial intelligence (AI)-assisted approaches for reconfigurable cell-free massive MIMO networks, with particular emphasis on autonomous self-configuration, efficient resource utilisation and resilience.
The research will investigate how distributed APs can dynamically adapt their operating modes, cooperation strategies and resource allocation in response to changing network conditions. Key challenges include adaptive AP clustering, user association, cooperative beamforming, power allocation, interference management and distributed resource management. Mathematical frameworks will be developed to characterise the impact of network reconfiguration on spectral efficiency, energy efficiency, connectivity and resilience, particularly under imperfect channel knowledge, signal blockage and temporary AP unavailability.
To address these challenges, the project will explore advanced mathematical optimisation and AI-based approaches, including deep reinforcement learning, graph neural networks and potentially large language models (LLMs). Particular attention will be given to investigating how AI-assisted decision-making can support autonomous network management and self-configuration, while optimisation algorithms determine efficient resource-allocation strategies. The proposed methods will be evaluated in terms of network performance, computational complexity, scalability and adaptability.
The research will primarily involve theoretical analysis, mathematical modelling, algorithm development and MATLAB/Python-based simulations. Expected outcomes include novel self-configuration frameworks, adaptive optimisation algorithms and intelligent resource-management strategies for efficient and resilient cell-free massive MIMO networks.
Eligibility
Applicants must meet the University of Manchester's PhD academic entry requirements and the eligibility criteria of the University of Manchester–China Scholarship Council (CSC) scheme. A Master's degree is desirable, and previous research experience in wireless communications, signal processing, mathematical optimisation, cell-free massive MIMO or machine learning would be advantageous.
Funding
This 3.5-year PhD project is for self-funded or externally funded students.
Some students may be eligible for the China Scholarship fee waiver scholarship. As these funding opportunities require a nomination from your supervisor, please ensure they are aware that you would like to be considered.
For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.
We recommend that you apply early as the advert may be removed before the deadline.
Before you apply
We strongly recommend that you contact the supervisor for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.
How to apply
Apply online through our website: https://uom.link/pgr-apply-2425
When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.
Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.
After you have applied you will be asked to upload the following supporting documents:
- Final Transcript and certificates of all awarded university level qualifications
- Interim Transcript of any university level qualifications in progress
- CV
- Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
- Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
- English Language certificate (if applicable)
If you have any questions about making an application, please contact our admissions team by emailing [contact details available in the full listing].
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.
We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
