9 papers
A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff
Zijian Liang, Kai Niu, Changshuo Wang +2
The fundamental limit of natural signal compression has traditionally been characterized by classical rate-distortion (RD) theory through the tradeoff between coding rate and recon…
DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representations
Kailin Tan, Jincheng Dai, Sixian Wang +5
Generative joint source-channel coding (GJSCC) has emerged as a new Deep JSCC paradigm for achieving high-fidelity and robust image transmission under extreme wireless channel cond…
A Theoretical Framework for Rate-Distortion Limits in Learned Image Compression
Changshuo Wang, Zijian Liang, Kai Niu +1
We present a novel systematic theoretical framework to analyze the rate-distortion (R-D) limits of learned image compression. While recent neural codecs have achieved remarkable em…
Error-Resilient Semantic Communication for Speech Transmission over Packet-Loss Networks
Zhuohang Han, Jincheng Dai, Shengshi Yao +5
Real-time speech communication over wireless networks remains challenging, as conventional channel protection mechanisms cannot effectively counter packet loss under stringent band…
Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook
Ping Zhang, Kai Niu, Yiming Liu +8
Artificial intelligence (AI) is expected to serve as a foundational capability across the entire lifecycle of 6G networks, spanning design, deployment, and operation. This article…
Neural Coding Is Not Always Semantic: Toward the Standardized Coding Workflow in Semantic Communications
Hai-Long Qin, Jincheng Dai, Sixian Wang +5
Semantic communication, leveraging advanced deep learning techniques, emerges as a new paradigm that meets the requirements of next-generation wireless networks. However, current s…