MDJSCC: Multiscale Feature Learning-Based Semantic Communications for Maritime Vessel Networks

Maritime vessel networks face critical communication challenges due to severe signal attenuation induced by dynamic oceanic conditions, including vessel mobility, wave disturbances, and atmospheric variability. These conditions result in high transmission latency and poor communication reliability,...

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Veröffentlicht in:	IEEE sensors journal Jg. 25; H. 22; S. 42251 - 42264
Hauptverfasser:	Li, Shibao, Wang, Xinkun, Li, Dongyang, Wang, Chongyang, Meng, Zihan, Zhu, Jinze, Li, Lianghai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 15.11.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Communication Computer architecture Data exchange Deep learning Encoding Feature extraction Joint source–channel coding marine communication Maritime communications maritime vessel networks Multilayers Networks Reliability Sea vessels semantic Semantic communication Semantics Sensors Signal to noise ratio Wireless communication
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
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