Semantic Communication System Based on Meta-Learning Framework

With the continuous advancements in deep learning technology, semantic communication, particularly deep joint source-channel coding (DJSCC), has garnered significant attention for its potential to enhance compression efficiency, reduce transmission delays and simplify system complexity. However, dee...

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Veröffentlicht in:	IEEE communications letters Jg. 29; H. 7; S. 1684 - 1688
Hauptverfasser:	Xie, Wenwu, Zhang, Tao, Xiong, Ming, Wang, Ji, Yang, Liang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Communications systems Complexity Convolutional neural networks Data models Decoding deep joint source-channel coding Deep learning Encoding few-shot meta-learning Numerical models Receivers Semantic communication Semantics Training Vectors
ISSN:	1089-7798, 1558-2558
Online-Zugang:	Volltext
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