# Semantic Change Driven Generative Semantic Communication Framework

arXiv: https://arxiv.org/abs/2309.12775 · HTML: https://mlsanity.com/paper/2309.12775/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2023.

## Built on by (most cited first, 5 of 5)

- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Agent-driven Generative Semantic Communication with Cross-Modality and Prediction](https://mlsanity.com/paper/2404.06997/) (2024)
- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Generative Semantic Communication for Text-to-Speech Synthesis](https://mlsanity.com/paper/2410.03459/) (2024)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)

## Abstract (arXiv)

The burgeoning generative artificial intelligence technology offers novel insights into the development of semantic communication (SemCom) frameworks. These frameworks hold the potential to address the challenges associated with the black-box nature inherent in existing end-to-end training manner for the existing SemCom framework, as well as deterioration of the user experience caused by the inevitable error floor in deep learning-based SemCom. In this paper, we focus on the widespread remote monitoring scenario, and propose a semantic change driven generative SemCom framework. Therein, the semantic encoder and semantic decoder can be optimized independently. Specifically, we develop a modular semantic encoder with value of information based semantic sampling function. In addition, we propose a conditional denoising diffusion probabilistic mode-assisted semantic decoder that relies on received semantic information from the source, namely, the semantic map, and the local static scene information to remotely regenerate scenes. Moreover, we demonstrate the effectiveness of the proposed semantic encoder and decoder as well as the considerable potential in reducing energy consumption through simulation based on the realistic F composite channel fading model. The code is available at https://github.com/wty2011jl/SCDGSC.git.