Junting Chen - Professor Communication systems
Date: - Location: Eurecom
Abstract: How to enhance the intelligence for wireless communication networks? One promising direction is to fuse more environmental information to the network, such as building radio maps, a data model that describes the wireless communication quality between any transmitter and receiver location pair. The technology has been successfully used for network planning, spectrum management, and fingerprint localization for over 20 years. However, conventional radio map techniques were limited to power spectrum maps and require precise location labels for construction and application. In this talk, we attempt the following two questions: Can we reconstruct and update a radio map from pilot sequences without precise location labels, and can radio map help reduce pilots for CSI tracking. We will start from an indoor scenario, where we develop a region-based radio map from received signal strength (RSS) measurements without location labels. A signal subspace model with a sequential prior is constructed for the RSS data, and an integrated segmentation and clustering algorithm is developed, which is shown to find the globally optimal solution in a special case. We demonstrate a reduction of region localization error by roughly 50% compared to existing schemes. In the outdoor scenario, we study the problem of radio-map-embedded CSI tracking and radio map construction without the assumptions of stationary CSI statistics and precise location labels. Using radio maps as the prior information, we develop a radio-map-embedded switching Kalman filter (SKF) framework that jointly tracks the location and the CSI with adaptive beamforming for sparse CSI observations under reduced pilots. For radio map construction without precise location labels, the location sequence and the channel covariance matrices are jointly estimated based on a Hidden Markov Model (HMM). An unbiased estimator on the channel covariance matrix is found. Numerical results on ray-traced MIMO channel datasets demonstrate that using 1 pilot in every 10 milliseconds, an average of over 80% of capacity over that of perfect CSI can be achieved for a user moving at 36 km/h at a 20 dB signal-to-noise ratio (SNR). Furthermore, the proposed radio-map-embedded CSI model can reduce the localization error from 30 meters from the prior to 6 meters for radio map construction. Short bio: Junting Chen (S’11–M’16) 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. He served as a patent consultant for Nokia in 2018. 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 near 100 papers in leading journals and conference proceedings, and has contributed to over 10 patents. He was a recipient of several province-level and national-level talent awards. 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.
