– Ericsson-Carleton Partnership Advances 5G Wireless Communications
Massive multiple-input multiple-output (M-MIMO) can serve a large number of users simultaneously with high spectrum and energy efficiency, making it a key technology for 5G wireless networks to achieve high throughput. With an ever-increasing number of antennas, the overhead and complexity of the processing required for channel estimation also “increases exponentially”. In this project we evaluate the feasibility of applying Distributed Machine Learning (DML) combining edge computing technology to conduct channel estimation in 5G massive MIMO through comparing the performance improvement on throughput, latency, as well as the accuracy of the results. Through the evaluation on the performance gains or losses of the proposed solutions over a centralized machine learning solution, this project shall build a solid foundation leading to the next stage of a bigger research program that is the implementation of a suitable architecture as well as a software platform that maximizes the potential of machine learning-based solutions to best elevate massive MIMO technology with the highest throughput, lowest latency and maximized data processing speed and accuracy while preserving data privacy.
– Contact Tracing System (Covid-19 Quarantine/Test Notification System)
Supported by NSERC Alliances Covid-19 Grant and CU Covid-19 Rapid Response Research Grant
– COVID-19 Mobile App that delivers exposure alerts but no personal information:
– Cyber Security in Critical Infrastructures
Supported by NSERC Discovery Grant
A distributed system is a system whose components are located on different networked entities (e.g., computers, sensors) that communicate and coordinate their actions by exchanging messages. These entities have a shared state, operate concurrently and can fail independently without affecting the whole system’s uptime. With the ever-growing technological expansion of the world, distributed systems are becoming more and more widespread.
In past years, I have addressed the protection and resilience of distributed systems such as Clouds, Data Centres, Mobile Agent and Actuator Networks, Vehicular and Wireless Sensor Networks. Building on this expertise, in the medium term, I plan to address various cyber security issues and develop attack resilient solutions for a very important type of distributed systems, namely the industrial automated control systems and networks that are monitoring and controlling the operation of critical infrastructures in different sectors. These include the Energy sector (e.g., oil pipelines, nuclear plants), the Financial sector, Government Operations, Water Supply, Health Systems and more. Cyberattacks against critical infrastructures have increased significantly in the last decade and caused serious damage with very important social and economic losses. However, research on protecting the control systems of critical infrastructures against cyberattacks is still at an early stage and faces many open technical challenges.