Information-theoretic and machine learning methods for low-power communication systems


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The unprecedented expansion of wireline and wireless networks has resulted in a tremendous increase in energy consumption and left a significant environmental footprint.

The use of low-resolution analog-to-digital converters (ADCs) has gained significant research interest because it addresses practical problems and scalability issues in 5G core technologies (including massive MIMO, mmWave communication, Internet-of-Things) such as massive data processing, high power consumption, and cost.

This PhD project forms part of a cluster collaboration between the University of Toronto and the University of Melbourne titled Communication and Computation: Two sides of one tapestry.

Project goals

The goals of this project are to:

  1. To take information-theoretic approaches to investigate the fundamental limit of systems with quantized input/output
  2. Use modern machine learning methods (e. g. deep learning) to solve complex optimization problems to improve the design and performance of such systems.

Supervision team

*Click on the researcher's name above to learn more about their publication and grant successes.

Who we are looking for

We are seeking a PhD candidate with the following skills:

  • Demonstrated experience in the field of communication systems, machine learning, and information theory
  • Demonstrated ability to work independently and as part of a team
  • Demonstrated time and project management skills
  • Demonstrated ability to write research reports or other publications to a publishable standard (even if not published to date)
  • Excellent written and oral communication skills
  • Demonstrated organisational skills, time management and ability to work to priorities
  • Demonstrated problem-solving abilities
  • The ability to work independently and as a member of a team.

Further details

The PhD candidate will benefit from the combined expertise of the project supervisors, and the embedding into two research environments.

Professor Jamie Evans and Dr Jingge Zhu at the University of Melbourne, and Professor Wei Yu and Professor Stark Draper at the University of Toronto will all contribute expertise in information theory, communication theory and machine learning to this project.

This PhD project will be based at the University of Melbourne with an 18-24 month stay at the University of Toronto.

The candidate will be enrolled in the PhD program at the Department of Electrical and Computer Engineering at the University of Toronto, and in the PhD program at the Department of Electrical and Electronic Engineering at the University of Melbourne.

To apply for this joint PhD opportunity, and to view the entry requirements, visit How to apply.

First published on 31 October 2021.

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