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.


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    Titel :

    Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling


    Beteiligte:
    Wan, Haowen (Autor:in) / Yang, Qianqian (Autor:in) / Tang, Jiancheng (Autor:in) / Shi, Zhiguo (Autor:in)


    Erscheinungsdatum :

    07.10.2024


    Format / Umfang :

    1199470 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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