Massive Signal and Data Cooperation for Densified Wireless Networks
As the volume of traffic and demand for wireless networks increases, this project aims to create a sustainable supply of data through development of new theories and algorithms in cooperative data and signal processing.
Our heavy reliance on wireless communications technologies continues to rise in daily life. Yet, wireless communications networks will come to a standstill unless more spectra are made available and radical solutions are sought to meet the growing demand for mobile data. To address this crisis, most existing schemes follow the same idea of adding new infrastructure, such as millimetre-wave and massive MIMO systems. However, to rely on expanding infrastructure is neither sustainable nor scalable to tackle the future wireless challenges beyond 2020. Instead, we must explore the potential of massive network data and signals.
The aim of this project is to provide the theoretical foundation as well as practical optimisation algorithms of massive cooperation for utilising the vast amount of contextual data and signals available in densified wireless networks. This project will help realise the perception of infinite capacity, lead to highly efficient usage of spectrum and energy, and improve user experience to the benefit of telecom industries.
To tackle the challenges and benefit from massive cooperation, this project first breaks the deadlock of signalling overhead and enables massive scale cooperation. This is followed by design proactive resource allocation via cooperative data processing, and optimisation and evaluation of holistic data and signalling.
Dr Gan Zheng - Reader in Signal Processing for Wireless Communications
"This is an exciting and adventurous project because we will turn the unexplored abundant data and signals into proactive actions that dramatically improve the efficiency of wireless networks. It will transcend traditional boundaries of information and communications theory, signal processing, and communication network data analytics."