CHEN Wei of School of Electronic and Information Engineering, Beijing Jiaotong University

Abstract:

Semantic communications is considered as a promising technology to increase the efficiency of next-generation communication systems, particularly targeting human-machine and machine-type communications. In contrast to the source-agnostic approach of conventional wireless communication systems, semantic communication seeks to ensure that only the relevant information for the underlying task is communicated to the receiver. Considering that most semantic communication applications have strict latency, bandwidth, and power constraints, a prominent approach is to model them as a joint source-channel coding (JSCC) problem. Although JSCC has been a long-standing open problem in communication and coding theory, remarkable performance gains have been shown recently over existing separate source and channel coding systems, particularly in low-latency and low-power scenarios. Recent progress is thanks to the adoption of deep learning techniques for joint source-channel code design that outperform the concatenation of state-of-the-art compression and channel coding schemes, which are results of decades-long research efforts. In this talk, I will introduce deep learning based JSCC (DeepJSCC) architecture for semantic communications, introduce its design principles and benefits, and highlight the GAP (Generalization, Adaptability, Practicality)  between ideal and reality.

Biography:

Professor Wei CHEN is a Professor in the School of Electronic and Information Engineering, Beijing Jiaotong University, China, and also with the State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, China. He received the B.Eng. degree and M.Eng. degree from Beijing University of Posts and Telecommunications, China, in 2006 and 2009, respectively, and the Ph.D. degree in Computer Science from the University of Cambridge, UK, in 2013. He was a Research Associate with the Computer Laboratory, University of Cambridge from 2013 to 2016. He is the recipient of the 2023 IEEE/CIC ICCC Best Paper Award, the 2022 China's Top 10 scientific and technological developments in the field of information and communication, the 2019 CCF-Tencent Rhino Bird Innovation Award, the 2017 International Conference on Computer Vision (ICCV) Young Researcher Award, the 2013 IET Wireless Sensor Systems Premium Award. His current research interests include massive access, semantic communications, AL/ML for PHY and sparse signal processing. He serves as the Leading Guest Editor for IEEE JSTSP on Intelligent Signal Processing and Learning for Next Generation Multiple Access. He is a Senior Member of IEEE, and an IEEE Communication Society Distinguished Lecturer. His current research interests include semantic communications, massive access, AL/ML for PHY and sparse signal processing.

Email:

Address:School of Electronic and Information Engineering, , Beijing Jiaotong University, Beijing, China