5 papers
Bernoulli Flow Models: Self-Consistent Generative Modeling for Binary Data
Hao Mo, Liying Yang, Shumin Yao +4
Binary diffusion models typically require many function evaluations (NFEs) to generate high-quality samples, making inference costly. Reducing NFEs without sacrificing sample quali…
Unlocking High-Fidelity Analog Joint Source-Channel Coding on Standard Digital Transceivers
Shumin Yao, Hao Chen, Yaping Sun +4
Analog joint source-channel coding (JSCC) has demonstrated superior performance for semantic communications through graceful degradation across channel conditions. However, a funda…
Zero-Shot Knowledge Base Resizing for Rate-Adaptive Digital Semantic Communication
Shumin Yao, Hui Du, Lifeng Xie +4
Digital semantic communication systems, which often leverage the Vector Quantized Variational Autoencoder (VQ-VAE) framework, are pivotal for future wireless networks. In a VQ-VAE-…
SCDM: Score-Based Channel Denoising Model for Digital Semantic Communications
Hao Mo, Yaping Sun, Shumin Yao +6
Score-based diffusion models represent a significant variant within the diffusion model family and have seen extensive application in the increasingly popular domain of generative…
Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer
Peigen Ye, Yaping Sun, Shumin Yao +3
Codebook-based generative semantic communication attracts increasing attention, since only indices are required to be transmitted when the codebook is shared between transmitter an…