Toshiaki Koike-Akino and Pu Wang (Mitsubishi Electric Research Laboratories (MERL), USA)
Abstract:
Recent millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies used in fifth generation and beyond (B5G) can achieve high resolution in both time and angular domain, bringing the "integrated sensing and communications (ISAC)" a viable concept. In particular, Wi-Fi-based human monitoring has received much attention due to the decreasing cost and less privacy concerns compared with camera-based approaches. Modern deep neural networks (DNNs) have made Wi-Fi-band signals useful for user identification, emotion sensing, and skeleton tracking. This tutorial reviews the trends, solutions, and limits of DNN for ISAC to discuss the potential future "post-DNN era", by introducing an emerging quantum machine learning (QML) in step-by-step demonstrations.
Short bios:
Toshiaki Koike-Akino (Senior Member, IEEE) received the B.S. degree in electrical and electronics engineering, the M.S. and Ph.D. degrees in communications and computer engineering from Kyoto University, Kyoto, Japan, in 2002, 2003, and 2005, respectively. During 2006–2010, he was a Postdoctoral Researcher with Harvard University, Cambridge, MA, USA, and joined MERL, Cambridge, MA, USA, in 2010. His research interests include signal processing for data communications and sensing. He was the recipient of the YRP Encouragement Award 2005, the 21st TELECOM System Technology Award, the 2008 Ericsson Young Scientist Award, the IEEE GLOBECOM’08 Best Paper Award in Wireless Communications Symposium, the 24th TELECOM System Technology Encouragement Award, and the IEEE GLOBECOM’09 Best Paper Award in Wireless Communications Symposium. He is a Fellow of Optica (formerly OSA).
Pu (Perry) Wang (Member, IEEE) received Ph.D. degree from Stevens Institute of Technology in 2011. He was an intern at the Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, in the summer of 2010. Before returning to MERL, he was a Research Scientist in the Mathematics and Modeling Department of the Schlumberger-Doll Research, Cambridge, MA, contributing to developments of logging-while-drilling Acoustics/NMR products. His current research interests include signal processing, Bayesian inference, statistical learning, and their applications to mmWave/THz/Wi-Fi sensing, wireless communications, networks and automotive applications. He received the IEEE Jack Neubauer Memorial Award from IEEE Vehicular Technology Society in 2013 for the paper “Multiantenna-assisted spectrum sensing for cognitive radio”. He is currently an Associate Editor for IEEE Signal Processing Letters and an Associate Member of IEEE SPS Sensor Array and Multichannel (SAM) Technical Committee.