Wireless AI for Ultra-Reliable and Low-Latency Communications in 6G
Date and time: 2 September, 12:00 – 13:00 CEST (UTC +2)
Speaker: Changyang She, The University of Sydney
Title: Wireless AI for Ultra-Reliable and Low-Latency Communications in 6G
Meeting ID: 695 6088 7455
Abstract: As one of the three typical application scenarios in 5G, ultra-reliable and low-latency communications (URLLC) will lay the foundations for many mission-critical applications, such as vehicle safety applications, industrial Internet-of-Thing, and remote healthcare. The state-of-the-art mobile communication systems can achieve a 1-millisecond delay in radio access networks but cannot fulfil the end-to-end delay and overall reliability requirements of URLLC. By integrating knowledge in wireless communications and networking into deep learning, we investigated 1) how to guide the learning algorithms with knowledge, including models, analysis tools, and optimization frameworks, 2) how to handle the mismatch between theoretical models and real-world networks with data-driven learning algorithms, 3) how to meet the conflicting requirements of URLLC with novel methodologies.
Bio: Dr Changyang She received his B. Eng degree in Honours College (formerly School of Advanced Engineering) of Beihang University (BUAA), Beijing, China in 2012 and PhD degree in School of Electronics and Information Engineering of BUAA in 2017. From 2017 to 2018, he was a postdoctoral research fellow at the Singapore University of Technology and Design. From 2018 to 2021, he was a postdoctoral research associate at the University of Sydney. He is the recipient of the Australian Research Council (ARC) Discovery Early Career Research Award (DECRA). Since 2021, he serves as an ARC DECRA fellow at the University of Sydney. His research interests lie in the areas of ultra-reliable and low-latency communications, deep learning in wireless networks, Industrial Internet-of-Things, and Tactile Internet.