Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling
This work addresses transmission efficiency for semantic communication systems, representing an incremental advancement by applying PGM techniques to a known bottleneck.
The paper tackles the problem of inefficient semantic communication by proposing a probabilistic graphical model (PGM)-based approach that compresses predictable semantic information and reconstructs it at the receiver, resulting in significant improvements in transmission efficiency while maintaining image quality.
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.