5 citations · 12 across the 7 of their papers we have counts for
8 papers
Tokenized Joint Source-Network Coding for Loss-Resilient Video Streaming
Ke Yang, Sixian Wang, Kailin Tan +4
For decades, conventional video streaming systems have relied on packet-level forward error correction (FEC) codes and retransmissions to ensure reliable video delivery. However, i…
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…
Rate-Distortion-Perception Controllable Joint Source-Channel Coding for High-Fidelity Generative Communications
Kailin Tan, Jincheng Dai, Zhenyu Liu +5
End-to-end image transmission has recently become a crucial trend in intelligent wireless communications, driven by the increasing demand for high bandwidth efficiency. However, ex…
DiffCom: Channel Received Signal is a Natural Condition to Guide Diffusion Posterior Sampling
Sixian Wang, Jincheng Dai, Kailin Tan +3
End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical cond…
WITT: A Wireless Image Transmission Transformer for Semantic Communications
Ke Yang, Sixian Wang, Jincheng Dai +3
In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Prev…
Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding
Jincheng Dai, Sixian Wang, Ke Yang +5
The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned…
Nonlinear Transform Source-Channel Coding for Semantic Communications
Jincheng Dai, Sixian Wang, Kailin Tan +4
In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it ca…
Learning to Decode Protograph LDPC Codes
Jincheng Dai, Kailin Tan, Zhongwei Si +4
The recent development of deep learning methods provides a new approach to optimize the belief propagation (BP) decoding of linear codes. However, the limitation of existing works…