Principal Supervisor (s): Dr Nidhi Simmons
Contact Details: [Email Address Removed]
Second Supervisor: Professor Simon Cotton
Project Introduction:
Ultra-reliable low-latency communications (URLLC) will be a core part of future wireless networks such as 5.5 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 URLLC 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.
Academic Requirements (depending on studentships Local and/or International)
• 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.
Application Closing date: 10/09/2021
Start Date: 01/11/2021
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