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​​AI-Driven Resource-Efficient Sensing and Communication Framework for NOMA-Enabled IoT Networks in 6G Systems​

Doctoral Training Grant Funding Information 

This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.    

All applicants will receive the same stipend irrespective of fee status. 

Application Closing Date: 


Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.  

How to Apply 

To apply, please follow the below steps:  

  1. Complete the BCU Online Application Form. 
  2. Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.  
  3. Upload two references to your online application form (at least one of which must be an academic reference). 
  4. Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s). 
  5. International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores. 

Frequently Asked Questions 

To help support you to complete your application, please consult the frequently asked questions below: 

Project Title: ​​AI-Driven Resource-Efficient Sensing and Communication Framework for NOMA-Enabled IoT Networks in 6G Systems​

Project Lead: ​​Dr Raouf Abozariba​ 

Project ID: ​​34 - 46459727​ 

Project Description: 

​​The rapid evolution of wireless communication technologies toward 6G has introduced unprecedented opportunities and challenges, particularly in the context of massive machine-type communications (mMTC) and smart environments. A key challenge lies in enabling intelligent, adaptive, and energy-efficient communication among billions of connected IoT devices with diverse quality of service (QoS) and quality of experience (QoE) requirements. Addressing this challenge requires advanced methods of resource allocation, spectrum management, and sensing-driven optimisation.

​This research aims to develop an AI-enabled framework for resource-efficient sensing and communication in NOMA (Non-Orthogonal Multiple Access)-based IoT networks, with a specific focus on real-time adaptability, scalability, and low energy consumption. By leveraging advanced machine learning techniques such as reinforcement learning, uncertainty-aware decision-making, and context-aware optimisation, the proposed system will intelligently allocate radio resources to heterogeneous IoT nodes while minimising spectrum waste and maintaining performance guarantees.

The project will be structured around three core objectives:

  • Designing a NOMA-based resource allocation scheme integrated with sensing feedback and QoE-aware prioritisation.
  • Developing AI algorithms for dynamic decision-making under uncertain and non-stationary environments, using data from distributed sensors.
  • Evaluating the proposed system in realistic simulation scenarios and potentially in hardware-in-the-loop platforms, using performance metrics such as energy efficiency, spectral efficiency, and latency.

​This research builds upon the recent work of Dr. Abozariba in intelligent resource management and spectrum sharing and extends it by introducing an AI-sensing synergy that is vital for 6G-era communication systems. The novelty of the project lies in its cross-layer integration of sensing data, AI-based control, and NOMA signalling to support a massive number of devices with minimal resource overhead. Expected outcomes include a set of practical algorithms and simulation frameworks that can guide future 6G standardisation efforts in the direction of intelligent, self-optimising IoT infrastructures. This work will also open new research avenues in energy aware sensing, autonomous spectrum allocation, and AI-driven network design.

Anticipated Findings and Contribution to Knowledge:

​This research is expected to deliver a novel AI-driven framework that enables efficient and adaptive communication in NOMA-enabled IoT networks. The system will combine advanced machine learning techniques with real-time sensing data to optimise the allocation of scarce radio resources under dynamic and uncertain network conditions. The anticipated findings include:

  • ​A new method for integrating sensing feedback into the decision-making process of wireless communication systems. ​
  • AI-based resource allocation algorithms that balance trade-offs between latency, energy consumption, and quality of service in dense IoT environments.
  • ​A scalable architecture that supports real-time reconfiguration and adaptation to varying network loads and environmental contexts.

The main contribution to knowledge lies in bridging three evolving research areas — artificial intelligence, wireless resource management, and environment-aware sensing — into a unified communication framework. Unlike many existing approaches that treat these aspects separately, this project will explore how context-awareness and AI can jointly improve communication efficiency, responsiveness, and sustainability in next-generation networks. Additionally, the project aims to contribute to the growing body of research on smart infrastructure by providing practical algorithmic models that could inform the design of future 6G systems and IoT standards. It will also offer insights into uncertainty modelling and self-optimising networks, two key challenges in emerging communication paradigms. Ultimately, this work is expected to push the boundaries of how intelligent, sustainable, and highly adaptive wireless systems are designed and implemented.

Person specification:

​​Entry Requirements: 

  • ​To apply for our Computing PhD Research Degree you should have, or expect to be awarded, a Master’s degree in a relevant subject area from a British or overseas university.  

  • ​Exceptional candidates without a Master’s degree, but holding a first class or upper second class Bachelor’s degree in a relevant subject area, may be considered.  

  • ​We also welcome enquiries from potential PhD researchers with appropriate levels of professional experience.  ​  

​ Essential Criteria: 

  • ​Solid foundation in mathematics, particularly linear algebra, probability theory, and optimization 

  • ​Understanding of wireless communication fundamentals, including modulation, multiple access techniques, and network protocols 

  • ​Basic knowledge of machine learning concepts and algorithms (supervised/unsupervised learning, neural networks) 

  • ​Programming proficiency in at least two relevant languages (Python, MATLAB, C++, or Java) 

  • ​Familiarity with signal processing principles and digital communications theory 

  • ​Understanding of IoT systems and network architectures 

  • ​Strong analytical and mathematical problem-solving abilities 

  • ​Demonstrated research aptitude through final year project and dissertation 

  • ​Excellent written communication skills evidenced by academic coursework and reports 

  • ​Critical thinking skills and ability to synthesise information from multiple sources 

  • ​Strong motivation for pursuing advanced research in wireless communications 

  • ​Self-directed learning capability and intellectual curiosity 

  • ​Good time management and organisational skills 

  • ​Ability to work both independently and collaboratively 

​Desirable Criteria:  

  • ​Prior exposure to NOMA, 5G/6G technologies, or advanced wireless communication systems 

  • ​Experience with reinforcement learning, optimization algorithms, or AI/ML frameworks (TensorFlow, PyTorch) 

  • ​Knowledge of resource allocation problems in communication networks 

  • ​Familiarity with simulation tools such as MATLAB Communications Toolbox, NS-3, Sionna or similar platforms 

  • ​Understanding of spectrum management and interference mitigation techniques 

  • ​Research experience through internships, final year projects, or publications 

  • ​Participation in relevant conferences, workshops, or technical competitions 

  • ​Experience with literature review and technical writing 

  • ​Familiarity with research methodologies and experimental design 

  • ​Industry experience through internships in telecommunications or technology companies 

  • ​Knowledge of quality of service (QoS) concepts and network performance metrics 

  • ​Understanding of energy efficiency considerations in wireless systems 

  • ​Familiarity with sensor networks and distributed systems 

  • ​Experience with version control systems (Git) and collaborative development 

  • ​Publications in undergraduate conferences or journals (highly desirable) 

  • ​Awards or recognition for academic achievement 

  • ​Leadership experience in technical societies or student organizations 

  • ​Relevant coursework in advanced topics such as information theory, network optimization, or machine learning 

  • ​Presentation skills and ability to communicate complex technical concepts 

  • ​Experience with technical documentation and report writing 

  • ​Resilience and perseverance in tackling challenging research problems 

  • ​Creativity and innovative thinking approach 

  • ​Cultural awareness and ability to work in diverse research environments 

  • ​Commitment to completing a rigorous 3-4 year PhD program ​ 

​The successful candidate will demonstrate strong potential for independent research while showing genuine enthusiasm for advancing the field of intelligent wireless communications and IoT systems. 

Overseas Applicants: 

International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.

Contact: 

If you have any questions or need further information, please use the contact details below:

For enquiries about the project content, please contact: ​Raouf.abozariba@bcu.ac.uk​

For enquiries about the application process, please contact: research.admissions@bcu.ac.uk