Group
Sustainable Digital Communication and Energy Systems
Unit(s) of assessment: General Engineering
Research theme(s): Digital, Technology and Creative | Safety and Sustainability
School: School of Science and Technology
Overview
5G and beyond (NextG) cellular communication networks have the potential to transform many aspects of society, from healthcare to transportation to entertainment, and its widespread adoption is expected to generate significant economic and social benefits. The increased speed and capacity, lower latency and improved network reliability will enable a wide range of new applications and services that require reliable high-bandwidth connections, such as virtual experiences and augmented reality, and live streaming allowing users to interact with digital content in real-time. Furthermore, NextG networks will enable the Internet of Things (IoT) to support a much greater number of connected devices leading to greater adoption of IoT technologies in smart cities and connected vehicles.
NextG will lead to improved healthcare supporting doctors to perform remote surgeries, medical consultations, monitor in real-time, and prognose and diagnose patients leading to faster more effective treatments. NextG will enhanced industrial automation allowing deployment of Industrial IoT devices that can improve automation, productivity, and efficiency in manufacturing and other industries. Overall, NextG will create new opportunities for businesses and individuals and shape the future of society in many positive ways that were previously not possible.
The research of the group is focusing on developing sophisticated transceivers using bandwidth efficient communication methods for NextG cellular systems and IoT sensor networks that bridge the gap between practically achievable channel utilisation and fundamental, information theoretical limits, on channel capacity.
We conduct research on massive Multiple-Input Multiple Output (MIMO) systems utilizing precoding, error control coding (ECC), and Orthogonal Frequency Division Multiplexing (OFDM) techniques. Furthermore, we use state-of-the-art big -data simulations that employ Stochastic Optimization (SO) methods and analysis, and Deep Neural Network (DNN) based Machine Learning (ML) approaches to estimate the channel states, to deal and manage interference, and solve intractable inverse modelling and networking problems with no theoretical closed-form solutions.