Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling

TL;DR

This paper introduces a semantic communication method using probabilistic graphical models to improve transmission efficiency by sharing common knowledge and reconstructing semantic information at the receiver.

Contribution

It presents a novel PGM-based semantic communication framework that enhances efficiency and maintains quality by compressing and reconstructing semantic features.

Findings

Significant improvement in transmission efficiency.

Effective semantic feature compression and reconstruction.

Maintained image quality during transmission.

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.