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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Vydané v:	IEEE communications letters Ročník 29; číslo 7; s. 1684 - 1688
Hlavní autori:	Xie, Wenwu, Zhang, Tao, Xiong, Ming, Wang, Ji, Yang, Liang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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