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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on cognitive communications and networking Jg. 11; H. 1; S. 90 - 104
Hauptverfasser:	Yang, Ke, Wang, Sixian, Dai, Jincheng, Qin, Xiaoqi, Niu, Kai, Zhang, Ping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!