The Ericsson-Carleton University Partnership for Research and Leadership in Wireless Networks is a collaborative effort that will conduct cutting-edge research to build more reliable, secure technology for the future of 5G wireless communications. The support of Ericsson will enable Carleton researchers and Ericsson employees to work together on a range of projects. The research will take place over a couple of phases, with studies in a variety of areas that will be important for the development of 5G networks such as machine learning, drone navigation and channel optimization.
The aim of this research is to investigate effective data-driven ML techniques for 5G networks.
This project will investigate cooperative algorithms, leveraging Beyond Visual Line of Sight (BVLOS) drone communications using 5G networks
The goal in this project is to perform research for and provide solutions to navigation and control of drones over 5G networks.
In this project, we propose developing a simulation tool to reconstruct temporal-spatial channels based on limited CSI feedback that has been collected from User Equipments (UEs) communicating over real networks.
In this project we will evaluate the feasibility of applying Distributed Machine Learning combining edge computing technology to conduct channel estimation in 5G massive MIMO.
In this project, we will investigate the use of Machine Learning and Deep Learning for spectrum sharing applications in 5G systems.
This project will combine a theoretically driven approach alongside analysis of field collected uplink sounding reference signal data (SRS) transmitted by several user terminals (UEs).
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