8 papers
SPAT: A Semantic Port-Aware Adaptive-Rate Transmission Protocol for Semantic Communication
Yunhao Wang, Shuai Ma, Bin Shen +4
With the evolution of 6G, semantic communication has emerged as a promising paradigm by prioritizing the delivery of task-relevant meaning over strict bit-level correctness. Howeve…
Optimally Bridging Semantics and Data: Generative Semantic Communication via Schrödinger Bridge
Dahua Gao, Ruichao Liu, Minxi Yang +3
Generative Semantic Communication (GSC) is a promising solution for image transmission over narrow-band and high-noise channels. However, existing GSC methods rely on long, indirec…
Modeling and Performance Analysis for Semantic Communications Based on Empirical Results
Shuai Ma, Bin Shen, Chuanhui Zhang +5
Due to the black-box characteristics of deep learning based semantic encoders and decoders, finding a tractable method for the performance analysis of semantic communications is a…
Structured IB: Improving Information Bottleneck with Structured Feature Learning
Hanzhe Yang, Youlong Wu, Dingzhu Wen +2
The Information Bottleneck (IB) principle has emerged as a promising approach for enhancing the generalization, robustness, and interpretability of deep neural networks, demonstrat…
Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels
Shuai Ma, Zhiye Sun, Bin Shen +5
In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction…
Task-Oriented Lossy Compression with Data, Perception, and Classification Constraints
Yuhan Wang, Youlong Wu, Shuai Ma +1
By extracting task-relevant information while maximally compressing the input, the information bottleneck (IB) principle has provided a guideline for learning effective and robust…