Research Programs

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.

P1.20s

Project Leader:
Professor Yonghui Li, University of Sydney

PhD Student:
Kou Tian, University of Sydney

Participants:

Interference modelling, detection, and mitigation for improving spaceborne SAR performance

Trusted Autonomous Satellite Operations

Synthetic aperture radar (SAR) is a key technology for Earth observation, that allows the unobstructed Earth observation and imaging including change detection and disaster management with high spatial resolution and independence of distance and weather conditions. Advancement in processing techniques and analyses will soon make it possible to have realtime monitoring. Due to the increasing ubiquity of wireless communications and the ever-increasing utilization of the radio spectrum, radio frequency interference is expected to become a major issue impacting spaceborne SAR technologies.

This research project aims to investigate interference topic in modern spaceborne SAR systems. It will construct a modelling framework to capture terrestrial interference using both analytic tools from stochastic geometry and simulation tools. It will then develop novel machine learning methods to detect interfering regions in reflected SAR signal based on both training samples, supervised and unsupervised learning methods. The research will thus develop interference mitigation techniques that will enhance SAR observation under the increasingly crowded radio spectrum.

The results of this project are expected to enhance the reliability of spaceborne SAR earth observation with direct applications in defence & security as well as in agriculture farming which is totally aligned with SmartSat CRC second research area “Earth observations from space” objectives and applications.

P2.41s

Project Leader:
Dr Akram Al-Hourani, Royal Melbourne Institute of Technology (RMIT)

PhD Student:
Nermine Hendy, Royal Melbourne Institute of Technology (RMIT)

Participants:

Small satellite energy-efficient on-board AI processing of hyperspectral imagery for early fire-smoke detection

SCARLET Lab

This research aims to provide a solution for energy-efficient AI-based on-board processing of hyperspectral imagery supporting automated early detection of fire smoke. We propose using modified and resampled MODIS imagery data that emulates the swath as well as spectral, spatial, and radiometric resolution of HyperScout-2 channel 1 hyperspectral imagery. In doing so, we intend to provide a solution that meets on-board processing limitations and up/downlink data transfer restrictions of the Kanyini – HyperScout-2/ with Intel’s Myriad X VPU chip.

Based on a semi-automatically created fire smoke training dataset, our proposed AI processing approach is performed at two levels: a) on-board feature and band selection, and b) ground AI neural network tasks – in order to optimize on-board processing and downlink data transfer. Expected outputs include on-board and ground AI algorithms for fire smoke detection, applicable for various hyperspectral imagery datasets.

P2.38

Project Leader:
Dr Stefan Peters, University of South Australia

Participants:

Satellite Proximity Surveillance System (SatProx)

Trusted Autonomous Satellite Operations

The project will develop the concept of an intelligent payload termed Satellite Proximity Surveillance System or SatProx that can automatically monitor the near region (< 30km) of a geostationary (GEO) satellite for potential approaches by an adversarial satellite. Such unexpected rendezvous manoeuvres, which occur surreptitiously at low relative speeds (< 0.7 m/s), could indicate hostile on orbit activities such as shadowing, interference and hijacking. By providing early warning, SatProx buys valuable time for the host satellite to engage in mitigation strategies.

The project will produce the design and specification of SatProx, including the sensor suite (e.g., optical, IR, thermal, LiDAR) and edge data processing subsystem that can support real-time inference on the data stream to automatically detect and raise alarm on potential incoming spacecraft. The project will also develop graphical simulation software that can render the GEO environment for the optical channel (visible spectrum) under varying conditions environmental conditions, including simulated encroachment by other adversarial satellites. Based on the simulation software, machine learning algorithms for real-time adversarial satellite detection and physical characterisation (shape, pose and trajectory of adversarial satellite) will be developed. The algorithms will then be demonstrated on the simulation environment with edge processing hardware (embedded GPU or FPGA) in the loop.

P2.36

Project Leader:
Professor Tat-Jun Chin, The University of Adelaide

Participants:

Using Blockchain and DRBs to Orchestrate an IoT Network

Secure IoT

In space communications, authentication plays an important role as a security technique that verifies and validates satellite identity. Single authentication can be weak and compromised. This project will investigate a novel two-layered multifactor authentication (2L-MFA) accompanied by a decentralized blockchain-based Satellite and IoT environment. The first-level authentication is for IoT devices and considers the secret key, geographical location, and the physically unclonable function (PUF). For lightweight and low latency support, proof-of authentication (PoAh) and elliptic curve Diffie-Hellman are used. Second-level authentication is for Satellite users and is subcategorized into four factor levels, namely identity, password, and the Nonce code (Blockchain). Matrix-based password enrolment in level 1, Elliptic Curve Digital Signature (ECDSA) in level 2, ensure Satellite-level authentication. Fuzzy logic will be deployed to validate the authentication and make the system stronger. The proposed 2L-MFA is evaluated in terms of registration time, login time, authentication time and authentication success rate.

P1.09s

Project Leader:
Professor Naveen Chilamkurti, La Trobe University

PhD Student:
Zachary Auhl, La Trobe University

Participants:

Spectrum Monitoring: Identifying Australia’s Needs and Opportunities

Cognitive Networks

The aim of this project is to identify areas of growth in satellite spectrum monitoring. It will provide advice on what technology capabilities can be developed within Australia and assess the market potential. The main outcome will be a report describing: potential users, the user requirements and a plan to meet those requirements.

The final report will also quantify the expected social and economic benefit to Australia of developing a sovereign space-based spectrum monitoring system.

The report will include a survey of relevant technologies that either already exist or can be developed in Australia. By matching the user requirements with available technologies, the report will provide advice on what research areas need to be focused on.

P1.24

Project Leader:
Professor Sam Drake, Flinders University

Participants: