Channel Estimation – Machine Learning Applied to the Physical Layer of Millimeter – Wave MIMO Systems
Channel estimation is challenging in millimeter wave systems because it combines both analog and digital beam forming (called the hybrid architecture). The objective of this challenge is to present solutions to the site-specific channel estimation problem with hybrid architectures. This talk will address the challenges of the problem as well as state-of-the-art solutions. Participants will be encouraged to design either a ML-based approach or a more conventional signal processing algorithm that can learn some priors from the provided training data set to provide high accuracy channel estimates with low training overhead during the testing phase. Furthermore, a description of the datasets for training and testing which is obtained from a specific urban location, and thus being site-specific will be provided. Finally, a description of the technical goals of this challenge will be provided. This will give insights into the trade-offs between data-driven and model-driven algorithms.
Speakers, Panelists and Moderators
NURIA GONZÁLEZ PRELCIC Associate Professor, Electrical and Computer Engineering DepartmentNorth Carolina State UniversityNURIA GONZÁLEZ PRELCICAssociate Professor, Electrical and Computer Engineering DepartmentNorth Carolina State UniversityDr. González Prelcic received her Ph.D. in Electrical Engineering in 2000 from the University of Vigo, Spain. She joined the faculty at NC State as an Associate Professor in 2020. She was previously an Associate Professor in the Signal Theory and Communications Department at the University of Vigo, Spain. She was also the founding director of the Atlantic Research Center for Information and Communication Technologies (atlanTTic) at the University of Vigo (2008-2017). She is an Editor for IEEE Transactions on Wireless Communications. She is an elected member of the IEEE Sensor Array and Multichannel Technical Committee. Her main research interests include signal processing theory and signal processing and machine learning for wireless communications: filter banks, compressive sampling and estimation, multicarrier modulation, massive MIMO, MIMO processing for millimeter-wave communication and sensing, including vehicle-to-everything (V2X), air-to-everything (A2X) and satellite MIMO communication. She is also interested in joint positioning and communication, joint sensing and communication, radar signal processing, radar and communications co-existence, multi-vehicle sensor fusion and autonomous navigation. She has published more than 80 papers in the topic of signal processing for millimeter-wave communications, including a highly cited tutorial published in the IEEE Journal of Selected Topics in Signal Processing which has received the 2020 IEEE SPS Donald G. Fink Overview Paper Award.