Invited Speakers

Assoc. Prof.Shuai Ma

Peng Cheng Laboratory, Shenzhen, China

Shuai Ma received the B.S. and Ph.D. degrees in communication and information systems from Xidian University, Xi'an, China, in 2009 and 2016, respectively.

From 2014 to 2015, he was a Visiting Scholar with the Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA. From 2016 to 2019, he has been an associate Professor with the School of Information and Control Engineering, at the China University of Mining and Technology, Xuzhou, China. From 2019 to 2022, he worked as a Postdoctoral Fellow with Telecom Paris, France.Since 2023, he has been an Associate Researcher at Peng Cheng Laboratory, Shenzhen, China. His research interests include semantic communications, visible light communications, and network information theory.

Title：Task-oriented Explainable Semantic Communications

Abstract：Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this talk, I will introduce an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we   derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the \beta_-variational autoencoder (\beta -VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our propose explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectivenessof our proposed robust semantic transmission scheme.