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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Bibliographic Details
Published in:	IEEE transactions on cognitive communications and networking Vol. 11; no. 1; pp. 90 - 104
Main Authors:	Yang, Ke, Wang, Sixian, Dai, Jincheng, Qin, Xiaoqi, Niu, Kai, Zhang, Ping
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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