Research Programs

Cognitive Satellite Radios

Cognitive Networks

The efficient and reliable utilization of the radio (electro-magnetic) spectrum for communications has been a long-standing problem, and with the introduction of various wireless (terrestrial) and satellite communication systems and technologies it is of utmost importance for us to address the issue of spectrum efficiency and intelligent spectrum sharing to pave way for the future of satellite (radio) communications. Cognitive radio
technology is an enabler to intelligently adopt satellite transmissions and receptions to improve the spectral efficiency by means of dynamic spectrum access (DSA) and to in turn improve the transmission throughput, resilience and the economic cost. Moreover, the broadcasting nature of such (satellite) transmissions and the inability to perfectly filter out in-band interferences have made satellite (radio) transmissions vulnerable, especially for the critical satellite communication infrastructure such as the defence satellite systems.

In this project we propose to develop and adopt advanced cognitive radio techniques for satellite communications to make satellite communication system intelligent and adaptive. This will improve the spectral efficiency of commercial satellite systems and maximise the throughput and availability of critical communication systems in congested and contested situations. The project makes important contributions to future, intelligent space networks that underpin Command and Control and Situational Awareness relating to Defence (including RMS StarShot) interests.

This project represents a substantial work as part of a number of activities anticipated in the cognitive network space. Importantly, the project incorporates a ‘stage-gate’ approach in development to ensure that focus is achieving its research outcomes, maintains industry alignment, and is coordinated with other, current and future-related projects.


Project Leader:
Professor Kandeepan Sithamparanathan


Emergency Communications for LunaSAR (RESARC Phase 2)


This project’s aim is to develop a concept design proposal for communications technology to help shape NASA’s LunaSAR requirements. This will derive from outcomes of the previous SmartSat project P1-07 (RESARC), and provide options to enhance form and function of the distress messaging system components in the challenging lunar environment. The activities consist of review of NASA’s preliminary concept of operations (CONOPS), architecture, operational parameters such as NASA’s sensor message definition, frequency band and satellite orbit characteristics, and constraints including size weight and power (SWaP); performance analysis for the LunaSAR communications subsystems including satellite payload; simulation and prototyping of key aspects, and; capture of design concept for NASA’s review and consideration. The expected outcome is that NASA will be presented with options to adopt best-in-class Australian technology for use within their LunaSAR system.


Project Leader:
Dr Mark Rice, Safety from Space


WildFireSat Mission and Australian Bushfire Management

Advanced Communication, Connectivity & IoT Technologies

The objective of this project is to assess the suitability of the Canadian WildFireSat mission for Australian Bushfire Management.


Project Leader:
Associate Professor Marta Yebra, Australian National University


Fusion of multi-platform Earth observation data for mapping of fire progression and post-fire vegetation recovery

EO Analytics

The key innovation of this project is the development of robust methods for the integration of radar-based EO data into current and emerging systems for monitoring the impact of fire on vegetation. Rapid fire extent mapping, including fire progression of large wildfires, will be based on dense time-series of synthetic aperture radar (SAR), optical data and machine learning. The research will also explore the capabilities of SAR and LiDAR data, integrated with optical data, for distinguishing structural characteristics of post-fire recovery dynamics.

The overarching focus of the research is on the integration of multi-sensor EO data to fill key gaps in operational monitoring of the impacts of wildfire. The project aims to support land and fire managers to make more informed decisions, by developing more accurate and timely measures of burnt area extent and tools for monitoring post-fire recovery.


Project Leader:
Dr Michael (Hsing-Chung) Chang, Macquarie University


Quantifying the Past and Current Major Australian Floods with SAR and Other Satellites

EO Analytics

Flooding is a common and extremely impactful event within Australia and around the world. For example, the March 2021 Australian floods are a series of floods that began from 18 March 2021 which have affected New South Wales, from the North Coast to the Sydney metropolitan area in the south, in a disaster described as a “one-in-100-year event”. Additionally, far-south and far-southeast communities in Queensland were also greatly affected by flooding and heavy rainfall.

The aim of this project is to develop and operationalise smart analysis of SAR and optical satellite imagery (primarily NovaSAR and Sentinel missions) to address time-critical applications such as flood mapping (2D) and floodplain water harvesting (3D), based on many years of research in this area by the project team since 2009. Project activities include feasibility studies, remote sensing software
development (analytic toolbox) and extensive case studies.

The expected outcomes are:

  1. A suite of near real-time, cross platform, scalable and operational tools for mapping floods with satellite remote sensing, ready for flood management agencies to takeover and/or private sector to commercialise, and improve volume estimate during the floodplain harvesting event for the Murray-Darling Basin states;
  2. A comprehensive report on feasibility studies to inform a Phase 2 project; and
  3. A comprehensive report on the case studies, targeting a range of users and promoting SmartSat CRC research through the mass media. The project brings together core partners such as UNSW, NSW Department of Planning and Environment, and Geoplex / Nova Systems, an ideal mix of academia, end user and geospatial service provider. The proposed project has also attracted strong support from the Federal Department of Agriculture, Water and Environment (DAWE) because of its significant national benefits (Letter of Support attached), as well as other key players such as Airbus and Geospatial Intelligence Pty Ltd.


Project Leader:
Linlin Ge, University of New South Wales


Deep Learning for Advanced Physical Layer Communications

Dynamic Networks

Driven by the booming amount of transmission information, modern communication systems have developed to the fifth generation, and are expected to integrate different radio access technologies, including the satellite component. As outlined in the 3GPP, the future integrated satellite and terrestrial architectures will lead to manifold advantages and make satellite communications essential to the evolution fo the 5G network. Thus, the future 5G satellite network is required to have low latency, high capacity, and strong adaptability to complex environments, and these requirements are aligned to our research objectives, “developing advanced communication networks which are efficient and stable”.

However, the traditional satellite communication systems only have a limited ability to face challenges in 5G scenarios, including high attenuation, the complex and unreliable communication environment and resulting transmission errors. Though there are some existing solutions for satellite communications to combat these channel impairments, they cannot work well enough for 5G. To ensure the accuracy, efficiency and reliability of the future wireless communications system, our research aims to develop DL based wireless physical layer frameworks (i.e., leveraging deep-learning to redesign the module of the conventional communication system) for performance improvement, which can also be used to implement 5G satellite communications.


Project Leader:
Professor Yonghui Li, University of Sydney

PhD Student:
Kou Tian, University of Sydney