8 citations · 38 across the 28 of their papers we have counts for
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eess.SP2023★ 3 cited
SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding
Ke Yang, Sixian Wang, Jincheng Dai +3
As one of the key techniques to realize semantic communications, end-to-end optimized neural joint source-channel coding (JSCC) has made great progress over the past few years. A g…
eess.SP2023
NeurJSCC Enabled Semantic Communications: Paradigms, Applications, and Potentials
Sixian Wang, Jincheng Dai, Xiaoqi Qin +2
Recent advances in deep learning have led to increased interest in solving high-efficiency end-to-end transmission problems using methods that employ the nonlinear property of neur…
eess.SP2023★ 2 cited
Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications
Sixian Wang, Jincheng Dai, Xiaoqi Qin +3
Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel c…