SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding

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 general trend in many recent works pushing the model adaptability or the application diversity of neural JSCC is based on...

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Vydané v:	IEEE transactions on cognitive communications and networking Ročník 11; číslo 1; s. 90 - 104
Hlavní autori:	Yang, Ke, Wang, Sixian, Dai, Jincheng, Qin, Xiaoqi, Niu, Kai, Zhang, Ping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Artificial neural networks attention mechanism Codec Codes Coding Configuration management Convolutional neural networks Image coding image communications Image resolution Joint source-channel coding Network latency Signal to noise ratio swin transformer Transformers Vectors Wireless communication
ISSN:	2332-7731, 2332-7731
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