CKM Construction via Interpolation and Supervised Learning | CKM Construction Professor Junting Chen скачать в хорошем качестве

CKM Construction via Interpolation and Supervised Learning | CKM Construction Professor Junting Chen

CKM is a data model that describes the wireless communication channel between any transmitter and receiver location pair. The challenge of CKM construction is the very limited availability of location-labeled channel measurement data, while a communication link may have its transmitter and receiver both moving in a 3-D space. Therefore, it is essential to develop data-efficient construction techniques that work in the regime with very sparse data. In the second talk of this series, we start from classical applications of radio maps and the approaches to build them. Then, we explore a recent research direction that constructs matrix and tensor representations for radio maps and develops sparse signal processing techniques to construct radio map from small samples. We will study the efficient construction of power spectrum maps and MIMO beam maps, and demonstrate their applications in localization in harsh environment. Junting Chen received the Ph.D. degree in electronic and computer engineering from The Hong Kong University of Science and Technology (HKUST), Hong Kong SAR China, in 2015, and the B.Sc. degree in electronic engineering from Nanjing University, Nanjing, China, in 2009. He is currently an Assistant Professor with the School of Science and Engineering, the Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong, China. Prior to joining CUHK-Shenzhen, he was a Postdoctoral Research Associate with the Ming Hsieh Department of Electrical Engineering, University of Southern California (USC), Los Angeles, CA, USA, from 2016–2018, and with the Communication Systems Department of EURECOM, Sophia-Antipolis, France, from 2015–2016. From 2014–2015, he was a visiting student with the Wireless Information and Network Sciences Laboratory at MIT, Cambridge, MA, USA. Dr. Chen works in the field of signal processing, optimization, and machine learning for wireless communications and localization. He focuses on applications in 5G/6G cellular communications and localization, underwater acoustic communication and localization, low-altitude air-to-ground integrated communications, massive MIMO, and radio maps. As a young scholar, Dr. Chen has published over100 papers in leading journals and conference proceedings, and has contributed to over 10 patents. He won the Charles Kao Best Paper Award in WOCC 2022. He currently serves as an editor for IEEE Transactions on Wireless Communications. He was recognized as the Top 2% Scientist in 2025.