Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911982329790464 |
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| author | Wan, Haowen Yang, Qianqian Tang, Jiancheng shi, Zhiguo |
| author_facet | Wan, Haowen Yang, Qianqian Tang, Jiancheng shi, Zhiguo |
| contents | In this paper, we propose a semantic communication approach based on probabilistic graphical model (PGM). The proposed approach involves constructing a PGM from a training dataset, which is then shared as common knowledge between the transmitter and receiver. We evaluate the importance of various semantic features and present a PGM-based compression algorithm designed to eliminate predictable portions of semantic information. Furthermore, we introduce a technique to reconstruct the discarded semantic information at the receiver end, generating approximate results based on the PGM. Simulation results indicate a significant improvement in transmission efficiency over existing methods, while maintaining the quality of the transmitted images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04499 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling Wan, Haowen Yang, Qianqian Tang, Jiancheng shi, Zhiguo Machine Learning In this paper, we propose a semantic communication approach based on probabilistic graphical model (PGM). The proposed approach involves constructing a PGM from a training dataset, which is then shared as common knowledge between the transmitter and receiver. We evaluate the importance of various semantic features and present a PGM-based compression algorithm designed to eliminate predictable portions of semantic information. Furthermore, we introduce a technique to reconstruct the discarded semantic information at the receiver end, generating approximate results based on the PGM. Simulation results indicate a significant improvement in transmission efficiency over existing methods, while maintaining the quality of the transmitted images. |
| title | Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.04499 |