Our research interest and expertise lie in signal processing and machine learning for wireless communications and radar. We focus on signal processing and machine learning as generic fields and develop tools for the analysis, modelling, estimation, and prediction of signals, finding applications in every area of science and technology. We particularly focus on transforming communication and radar technology, influencing various social and economic factors by providing mobile Internet access anywhere and anytime.

Our present research focuses on sensing and communications combined with digital twin and semantic communications as an intelligent framework for machine learning, particularly to develop robotic communications. We were among the first researchers to investigate the use of multiple antenna (the so-called MIMO) signal processing techniques for both base stations (transmitters) and handsets (receivers) in wireless networks to support large-capacity mobile Internet applications. Our early research on multiple antenna signal processing demonstrated how to achieve, in a practical way, a significant portion of the large capacity promised by information theory (equally the transmission environment) when using a large number of transmit and receive antennas in the same frequency band. This work led to award-winning publications, innovations, and industrial collaborations. Our work on reduced complexity MIMO transceiver architecture, called “Turbo-MIMO,” was tested at Bell Labs in New Jersey, USA, and demonstrated significant throughput compared to single antenna and other MIMO algorithms.