This project explores how artificial intelligence (AI) can be used to improve the way machines communicate visual information. The student will work closely with a senior researcher and join other undergraduate and postgraduate students, providing an opportunity to learn and collaborate in a supportive environment.
The aim of the project is to develop a system that can learn to communicate images efficiently using deep learning. Instead of focusing on transmitting every pixel perfectly, the system will learn to send only the most important features needed for a computer to correctly recognize the images content. The student will gain hands on experience in training neural networks, testing how telecommunication systems work within limited throughput, and comparing the performance with traditional image transmission methods.
By the end of the project, the student will have a solid understanding of both modern AI techniques and the principles of task oriented telecommunications while contributing to a first of its kind research study in this emerging area.
School
Electrical Engineering and Telecommunications
Research Area
Deep learning | Digital communications
Suitable for recognition of Work Integrated Learning (industrial training)?
No
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
The research will be conducted in the Wireless Communications Laboratory at UNSW, a world class facility at the forefront of wireless communications, signal processing, and information theory. The lab is equipped with high performance workstations, GPUs, and software defined radio platforms, enabling students to explore both theoretical concepts and practical system implementation. Students will have the opportunity to experiment with modern machine learning tools, apply AI driven approaches to communication systems, and see how abstract theories are realized in practice. The lab supports flexible experimentation through both simulations and real world testbeds, bridging the gap between theory and application.
The laboratory is led by Prof. Jinhong Yuan, Head of School, and includes a collaborative team of four postdoctoral researchers and engineers, alongside ten PhD, masters, and undergraduate research students. Research outcomes from the lab are regularly published in leading journals and conferences, providing students with insight into high impact research practices. This environment fosters close mentorship, teamwork, and active participation in ongoing projects, giving students hands on experience and a strong foundation in both communication and AI driven methods.
- An end to end emulation package via Python demonstrating accurate image classification in the receiving end.
- Semantic Communications: Principles and Challenges, https://arxiv.org/pdf/2201.01389
- Digital Communications by John Proakis