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

Updated: 2 months ago
Deadline: 30 Apr 2022

One funded project is available on Information-theoretic and machine learning methods for low-power communication systems with The University of Melbourne (UoM) and The University of Toronto (Canada). GRs participating in the joint program will be enrolled at both institutions and will spend a minimum of 18 months at the host institution. Funding includes a full scholarship, health insurance and mobility support.

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.


View or Apply

Similar Positions