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Intelligence for Ultra-Reliable Low-Latency Applications in 6G


   School of Electronics, Electrical Engineering and Computer Science

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  Dr Nidhi Simmons, Dr S L Cotton  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Ultra-reliable low-latency communications (URLLC) will be a core part of future wireless networks such as 5.5G and 6G. It will enable many exciting mission-critical applications such as autonomous driving, industry automation and remote surgery, to name but a few. Achieving URLLC from a physical layer perspective is very challenging as it must satisfy two conflicting requirements: low-latency and ultra-high reliability. Minimizing latency requires the use of short transmissions which in turn causes severe degradation in channel coding gain, whilst ensuring reliability requires more resources (retransmissions) which increases latency. Thus, enabling URLLC is a huge challenge facing future wireless technologies and will introduce a wide variety of demands in terms of system design.

Project Description:

In this project, you will have the opportunity to investigate and design practical techniques for achieving ultra-reliable low-latency communications taking in to account the uncertainty introduced by the transmission environment. Using machine-learning, you will build your own testbed to select the best transmission strategy and reduce end-to-end latency. Building this testbed will involve gathering wireless channel measurements, statistically analysing the data followed by designing, training, and validating models/neural networks.

Some key objectives include:

  • Understanding existing wireless channel models.
  • Understanding the trade-off between (a) device energy consumption, processing power and latency and (b) reliability and latency in mission-critical applications.
  • Conducting channel measurements for emergent URLLC applications such as autonomous driving and industry automation.
  • Build and train light-weight neural networks that can be used on resource constrained devices.

Research on intelligence for future wireless technologies/mission-critical applications is a popular research topic at the moment. Undertaking this PhD study, will provide the opportunity to develop skills in experimentation, data analysis and machine learning as well as wireless communications

Project Key Words

Artificial intelligence, autonomous driving, channel modelling, data analysis, deep learning, future wireless technologies, industry automation, low latency communications, machine learning, mission critical applications, wireless communications, URLLC, ultra reliable communications, 5G, 5G+, 6G.

Start Date: 01/10/22

Application Closing date: 28/02/22

For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at

https://go.qub.ac.uk/dfeterms

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required.

Experience with MATLAB and/or Python programming language will be beneficial.

Funding Notes:

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.

For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.

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