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...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE communications letters Vol. 29; no. 7; pp. 1684 - 1688
Main Authors:	Xie, Wenwu, Zhang, Tao, Xiong, Ming, Wang, Ji, Yang, Liang
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.07.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!