AI/ML-driven Resource Management Framework for 6G MEC-assisted Industrial IoT Networks
Eligibility: UK/International (including EU) graduates with the required entry requirements
Duration: Full-Time – Four years fixed term
Application deadline: 25 April 2024
Interview date: Will be confirmed to shortlisted candidates
Start date: September 2024
For further details, contact: Dr. Faouzi Bouali
Introduction
This is a four year collaborative studentship which requires the candidate to spend two full years based at Coventry University (UK) and two years based at A*STAR Research Institute (Singapore). The usual pattern is first and fourth years at Coventry and second and third year at A*STAR Research Institute.
Project details
Industrial internet-of-things (IIoT) use cases (e.g., self-driving cars and Industry 4.0) have stringent requirements (e.g., low-latency and high-reliability) that are out of reach of legacy connectivity solutions (e.g., Wi-Fi and 4G). While the advanced 5G features (e.g., time-sensitive networking (TSN) and ultra-reliable low-latency communication (URLLC)) can meet some of these requirements, they fall short in supporting the most demanding use cases.
In this context, three technological enablers can collectively overcome the limits of 5G. First, multi-access edge computing (MEC) allows to move the compute and analytics closer to the data, which reduces latency, alleviate traffic load on transport/core networks, and helps achieve privacy-preserving, enabling the dynamic deployment of edge applications. Second, Open RAN enables the automated closed-loop optimisation of the RAN, which is currently not possible with MEC.
Third, artificial intelligence (AI)/machine learning (ML) can collect and capitalize on the massive amount of data at the edge to achieve an efficient management, automation, and optimization of resources, while maintaining integrity and even ownership. These enablers, albeit useful, are complex and not straightforward to combine. Therefore, this project aims at constructing an AI/ML-driven Resource Management Framework for MEC-assisted IIoT networks, where synergies between these technologies are achieved in the IIoT context.
Funding
Coventry University and A*STAR jointly offer a fully-funded PhD studentship including tuition fees and stipend/bursary, that is open to both UK/EU and international graduates as part of the A*STAR Research Attachment Programme (ARAP).
Coventry University and A*STAR will only cover the stipend up to a maximum of two years each. Changes to the mobility pattern will only be considered under exceptional circumstances and can impact on the duration of the course and level of funding available. Should a candidate request any changes to mobility which results in the period spent in either the UK or Singapore extending beyond two years then the candidate is responsible for covering the stipend for that period.
Benefits
The successful candidate will receive comprehensive research training including technical, personal and professional skills.
All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.
Entry requirements
- A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
PLUS
- The potential to engage in innovative research and to complete the PhD within a 4 years.
- A minimum of English language proficiency (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component).
Additional requirements
- Strong background in wireless communications and networking with detailed knowledge of the protocol stack of 5G and Beyond (5G/B5G) networks and their latest architectural enhancements (e.g., multi-access edge computing (MEC), Open RAN and network slicing).
- Good familiarity with typical Industrial internet-of-things (IIoT) use cases and their associated requirements.
- Excellent programming and prototyping skills using e.g., Python, C/C++, Linux networking and community-based open-source software tools (e.g., Kubernetes, Docker, Git and Jenkins).
- Experience in carrying out link- and system-level simulations of wireless networks using publicly available tools (e.g., NS3, OMNeT++ and Matlab). Prior hands-on experience on testbeds based on software defined radios (SDRs) and open-source tools is an added plus.
- Knowledge of artificial intelligence (AI)/machine learning (ML) techniques. Prior experience implementing AI/ML algorithms using well-known frameworks (e.g., like PyTorch and TensorFlow) is an advantage.
- Aspiration to achieve high-quality research contributions and publications in leading conferences and journals. Prior research experience and publications are clear advantages.
How to apply
To find out more about the project, please contact Dr Faouzi Bouali
All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.
Apply to Coventry University
