应我校电子与信息工程学院孟维晓教授、陈舒怡副教授邀请，在IEEE ComSoc哈尔滨分会的支持下，北京交通大学电子信息工程学院教授陈为将来我校开展讲座。学术讲座将于2026年8月31日在金宝搏app官网下载科学园科技创新大厦（TIB）J507举行。陈为教授将围绕语义通信的信源信道联合编码问题，深入剖析其面临的挑战及关键技术，内容涵盖最新研究成果及未来发展趋势，具有很高的学术价值和启发性。此次讲座内容丰富，面向我校师生开放，诚挚邀请对语义通信领域感兴趣的老师和同学们积极参与。
讲座信息
报告：Joint Semantic Source Channel Coding: GAP Between Ideal and Reality
报告时间：2026年8月31日18：00-19：00
摘要: 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.
报告人简介：
Wei Chen is currently a Professor with Beijing Jiaotong University, Beijing, 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. Later, he was a Research Associate with the Computer Laboratory, University of Cambridge from 2013 to 2016. He is an IEEE Communication Society Distinguished Lecturer and an IEEE senior member. His current research interests include semantic communications, massive access, AL/ML for PHY and sparse signal processing.
陈为，北京交通大学电子信息工程学院教授、北京市高速铁路宽带移动通信工程技术研究中心副主任、国家铁路局宽带移动信息通信铁路行业重点实验室副主任、IEEE通信学会杰出讲师，英国剑桥大学博士、博士后，国家优秀青年科学基金获得者。主要从事宽带移动通信系统与专用移动通信、智能通信等领域的科研工作。研究成果荣获中国信息通信领域年度十大科技进展、中国通信学会科学技术奖自然科学二等奖、詹天佑铁道科学技术奖秦驰道专项奖青年奖等。
