Cancelled

T10: Statistical Inference via Gaussian Approximation in 6G Wireless Communication Systems: Bridging Theory to Practice

Organizer: Takumi Takahashi, Osaka University, Japan
Organizer: Hiroki Iimori, Ericsson, Japan
Organizer: Koya Sato, The University of Electro-Communications, Japan

Abstract: In the realm of sixth generation (6G) wireless communication systems, poised as the nexus connecting tangible reality with the virtual world, the necessity for advanced physical-layer signal processing is paramount. To achieve latency requirements from a variety of applications, a swiftly emerging framework seeking to design signal processing that adeptly balances low complexity and high-accuracy inference is needed, while seamlessly integrating statistical properties of radio propagation characteristics, underpinned by sophisticated mathematical tools. Yet, the landscape introduces added intricacies such as propagation characteristics shift with frequency band variations, the integration of multi-element and distributed antennas, and the introduction of novel access schemes, which may collectively amplify the complexity of inference problems. Navigating from the basics of probabilistic inference to cutting-edge research in this context poses a substantial challenge.

In the above context, this tutorial focuses on the widely embraced Bayesian inference approach, with the Gaussian approximation as its focal point. Covering the spectrum from fundamental principles to exemplars of the latest research, the tutorial discusses the use of advanced statistical inference techniques in 6G systems and applications, gaining insights not only into the latest trends in physical-layer signal processing but also acquiring the indispensable skills to craft adept probabilistic inference solutions.

Topic: message-passing algorithm, bilinear inference, Gaussian process regression, Bayesian optimization, grant-free access, cell-free massive MIMO, XL-MIMO.

Organizer’s bios:

Takumi Takahashi (Member, IEEE) received the B.E., M.E., and Ph.D. degrees in communication engineering from Osaka University, Osaka, Japan, in 2016, 2017, and 2019, respectively. From 2018 to 2019, he was a Visiting Researcher at the Centre for Wireless Communications, University of Oulu, Finland. In 2019, he joined as an Assistant Professor with the Graduate School of Engineering, Osaka University. His research interests include belief propagation, compressed sensing, signal processing, and wireless communications.

Hiroki Iimori (Member, IEEE) received his Ph.D. degree (special distinction) in electrical engineering from Jacobs University Bremen, Germany, in 2022, and his B.Eng. and M.Eng. degrees (Hons.) in electrical and electronic engineering from Ritsumeikan University, Kyoto, Japan, in 2017 and 2019, respectively. In 2020, he was a visiting scholar at the Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada. In 2021, he was a research intern at the Ericsson Radio S&R Research Laboratory, Yokohama, Japan, where he is now an experienced researcher. His research interests include optimization theory, wireless communications, and signal processing. He was awarded the YKK Doctoral Fellowship by the Yoshida Scholarship Foundation, the IEICE Young Researcher of the Year Award by the IEICE Smart Radio Committee in 2020, among others.

Koya Sato (Member, IEEE) was born in Miyagi, Japan, in 1991. He received the B.E. degree in electrical engineering from Yamagata University, in 2013, and the M.E. and Ph.D. degrees from The University of Electro-Communications, in 2015 and 2018, respectively. From 2018 to 2021, he was an Assistant Professor with the Tokyo University of Science. He is currently an Assistant Professor with the Artificial Intelligence eXploration Research Center, The University of Electro-Communications. His current research interests include wireless communication, distributed machine learning, and spatial statistics.