# Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling

arXiv: https://arxiv.org/abs/2408.04499 · HTML: https://mlsanity.com/paper/2408.04499/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Image2StyleGAN++: How to Edit the Embedded Images?](https://mlsanity.com/paper/1911.11544/) (2019)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Deep Learning for Joint Source-Channel Coding of Text](https://mlsanity.com/paper/1802.06832/) (2018)
- [SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing](https://mlsanity.com/paper/2112.02236/) (2021)
- [DeepWiVe: Deep-Learning-Aided Wireless Video Transmission](https://mlsanity.com/paper/2111.13034/) (2021)
- [Semantic-preserved Communication System for Highly Efficient Speech Transmission](https://mlsanity.com/paper/2205.12727/) (2022)
- [Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission](https://mlsanity.com/paper/2211.10287/) (2022)
- [Semantic Communication with Probability Graph: A Joint Communication and Computation Design](https://mlsanity.com/paper/2310.00015/) (2023)
- [Enhancing Image Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy](https://mlsanity.com/paper/2405.09234/) (2024)

## Abstract (arXiv)

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.