Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling
<|reference_start|>Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling: 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.<|reference_end|>
@article{wan2024knowledge-aided,
    title={Knowledge-Aided Semantic Communication Leveraging Probabilistic
  Graphical Modeling},
    author={Haowen Wan, Qianqian Yang, Jiancheng Tang, Zhiguo shi},
    journal={arXiv preprint arXiv:2408.04499},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04499},
    primaryClass={cs.LG}
}