Joint Semantic-Physical Neural Network for Underwater Communication
Semantic coding significantly enhances communication efficiency and reduces data traffic by transmitting essential semantic information. A joint semantic-physical neural network (JSPNN) structure is proposed for autonomous underwater vehicle communications to address two technique issues over the underwater environment, including the coding efficiency for real-time requirements and the environment applicability for transmitting limitations. Both the JSPNN structure and the construction of semantic knowledge base are illustrated. Finally, we indicate some forward issues on this topic.
Authors
- Yanglong Sun (ORCID: https://orcid.org/0000-0003-3668-6617)
- Yangzhe Liao (ORCID: https://orcid.org/0000-0003-2841-9578)
- Yi Han (ORCID: https://orcid.org/0000-0002-7826-9099)
- Zhibin Gao (ORCID: https://orcid.org/0000-0003-1878-9065)
- Fei‐Yun Wu (ORCID: https://orcid.org/0000-0002-0276-4093)
- Dan Song (ORCID: https://orcid.org/0000-0002-4096-3333)
- Zhiping Xu (ORCID: https://orcid.org/0000-0001-6837-4293)
Institutions
- Jimei University (CN)
- Wuhan University of Technology (CN)
Publication Details
- Journal
- IEEE Internet of Things Magazine
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1109/miot.2025.3592953
- Citations
- 1
- Primary Topic
- Underwater Vehicles and Communication Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.15