Research areas summary

RF wireless systems:

Satellite and space systems:

Optical wireless communications

Machine learning and Signal Processing:

Satellite communications

Digital connectivity has become the foundation of prosperity and an essential need for functioning societies. Despite this dependence, limitation on Internet access remains a prevalent issue, largely hinging on socioeconomic and geographic factors. A promising solution to attain global access equality is based on integrated terrestrial-satellite networks that rely on low Earth orbit (LEO) mega constellations. While the benefits of LEO constellations complement the shortcomings of terrestrial networks, their incorporation impacts the network design, adding complexity and challenges. Our research aim at developing new methods and techniques for satellite systems including:


Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) technology offers numerous benefits over optical imaging systems used for Earth observation. SAR can create high-resolution radar images of the Earth's surface, even in adverse weather conditions and independent of the sunlight. It is also capable of penetrating through clouds, fog, and vegetation, making it ideal for mapping and monitoring the Earth's terrain and infrastructure. SAR can thus be used in a variety of applications, including remote sensing, environmental monitoring, and disaster management. The unique benefit of SAR imaging is its ability to detect the smallest changes in the environment over time, allowing for long-term monitoring and assessment of natural and artificial changes to the landscape. Such advantages of SAR are only possible because it relies on the radio frequency spectrum, instead of the optical spectrum, to construct the reflectivity image of the observed scene. Another important difference to optical imagery is that SAR systems are active sensors, which means that they need to illuminate their target swath (region) by generating a relatively high-power signal and then listening to the very weak reflections bouncing back from the ground.  The topics that we are actively working on are:

Wireless optical communications

Traditionally, “wireless communication” normally refers to the systems and technologies using the radio frequency (RF) range. However, the RF range nowadays faces the congestion issue due to the scarcity of spectrum resources. On the other hand, optical wireless communications explore the electromagnetic spectrum in the optical frequency range, where hundreds of terahertz bandwidth are available, offering the possibility to support ultra-high-speed wireless communications. Optical wireless communications are also immune to conventional electromagnetic interference (EMI) and can be used in RF-hostile environments, providing a promising solution for beyond-5G. Our research focuses on advanced technologies in optical wireless communications, such as optical MIMO, advanced modulation and signal processing (both conventional and machine learning based), and silicon-based photonic integration of key devices and transceivers. Our work targets at various use-case scenarios, including data-centre networks, mobile fronthaul / backhaul, last-meter indoor connectivity, vehicular communications, underwater communications, and satellite communications and networks. 

Optoelectronics neuromorphics 

This capability relies on an ability to create autonomous sensing and decision-making hardware systems. These can be designed to interface to a broad range of satellite systems with a goal to create secure, efficient and smart operations while maintaining structural and mechanical integrity. We will build on key cross-disciplinary RMIT strengths in advanced materials, nanoelectronics, photonics, quantum systems and machine learning to develop a world-leading capability to service the needs of the Australian defence and space sector. The theme envisions to develop technologies that capture, analyse, process and communicate information coupled with decision making abilities using faster, lighter and smarter systems. Generating the information will rely on a multitude of sensing/detection capabilities and artificial vision catering across the electromagnetic spectrum. These technologies are required to ensure autonomous manoeuvrability, automated data analysis and maintaining satellite health via remote satellite health monitoring systems for informed decision making. The suite of technologies will open new avenues for navigation in extra-terrestrial environments thereby enhancing our capability to understand our planet and the universe in a more holistic manner. Our research in this area includes:


Reconfigurable Intelligent Surfaces

Using Reconfigurable Intelligent Surface (RIS), smart wireless networks can transmit information without generating new signals but recycling the incoming signal. However, as an emerging technology, fundamental analysis – in terms of rate, reliability, and efficiency – is needed to understand the performance of RIS-empowered wireless networks. Expected outcomes include new communication-theoretic models and the enabling technologies to realise them in practice. These smart environments have the potential to offer “greener” and more "seamless wireless connectivity" for the future wireless network.

Simultaneous Wireless Sensing and Communication

To investigate sensing, localisation and communication strategies to improve the performance of modern tactical radio networks. Such networks face all of the well-known design challenges of mobile ad-hoc networks (MANETs) but with added complication of a contested and adversarial operating environment. By exploiting the power of radio nodes to sense the radio spectrum, as well as to communicate over it, a distributed network of nodes can create a detailed picture of the surrounding radio-frequency (RF) environment: the nodes can work together to map the “RF weather”. Under this theme we design advanced sensing and localisation methods to accurately map the RF spectrum, and then exploit this map in communication system design.


AI for training efficacy and skill retention

This theme uses AI to optimize the training process of people. Using neural network models, we research physiological monitoring of training efficacy and skill retention in trainees. It creates a real-time physiological monitoring system to assess training progress and provide diagnostic feedback to trainees and instructors. It determines the link between task-specific performance, cognitive workload, and training efficacy. The research outcomes will be applied to identify the retention of skills in trainees and the need for re-training. Application examples include control tower personnel and pilot training, sports training, and customer service training

AI-aided human-machine decision-making

The project creates a distributed decision-making system of neural network units. The units will work on independent tasks based on different data modalities and labels. The outcomes from the units will support the final decision-making process. No arbitrary rules will be applied; the final decision will be made based on the information flow through the system and algebraic connections between the units. Application examples include multimodal medical diagnosis, physiological monitoring, multimodal cyber security, and person identification. A similar approach will also be used to model to predict the preferences of individuals or groups of people. It will determine how individuals choose among different alternatives and what drives their preferences. Factors contributing to the decision-making processes underlying how choices and preferences are made will be investigated. The model will provide an instrument to forecast public and personal demands. Application examples include areas such as planning, advertising, entertainment, and retailing.