Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling

• URL: http://arxiv.org/abs/2408.04499v1
• Date: Thu, 8 Aug 2024 14:50:48 GMT
• Title: Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling
• Authors: Haowen Wan, Qianqian Yang, Jiancheng Tang, Zhiguo shi,
• Abstract summary: We propose a semantic communication approach based on probabilistic graphical model (PGM) We evaluate the importance of various semantic features and present a PGM-based compression algorithm designed to eliminate predictable portions of semantic information.
• Score: 6.754511772924184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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