Integrated Sensing and Communications for Vehicular 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
The integrated sensing and communications (ISAC) technology is expected to be one of the key enablers of the sixth generation (6G) of wireless networks. ISAC refers to the use of radio signals to detect and estimate characteristics of objects in the environment, with the network acting as a “sensor” to comprehend the physical world it operates within.
Combining disjoint communication and sensing systems into one system brings numerous advantages covering multiple aspects, such as ubiquitous sensing and communication, reduced power consumption, smaller number of required antennas, less cabling requirements, lower cost, and higher spectral efficiency. This can be particularly beneficial for connected and autonomous vehicles (CAVs), which strongly rely on sensing and communication capabilities.
However, the current ISAC solutions cannot cope with the unique challenges (e.g., high mobility and stringent safety requirements) associated with CAVs, making them subject to high levels of interference. Therefore, this proposal will develop advanced physical layer solutions (i.e., advanced signal processing) and radio resource management (RRM) functionalities (e.g., multi-user access and resource allocation) for ISAC in vehicular environments. The performance of the developed solutions will be evaluated through extensive link-level (i.e., physical layer) and system-level (i.e., RRM) simulations and benchmarked against existing ISAC solutions.
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
Additional requirements
- Strong background in wireless communications and networking with solid knowledge of advanced signal processing techniques and radio resource management functionalities. Prior work on integrated sensing and com-munications (ISAC) is a plus.
- Excellent programming and prototyping skills with experience in carrying out link- and system-level simulations of wireless networks using publicly available tools (e.g., MATLAB, NS3, and OMNeT++).
- Good knowledge of artificial intelligence (AI)/machine learning (ML) techniques. Prior experience implementing AI/ML algorithms using well-known frameworks (e.g., PyTorch and TensorFlow) is an advantage.
- Aspiration to achieve high-quality research contributions and publica-tions 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.
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