# Enabling Training-Free Semantic Communication Systems with Generative Diffusion Models

arXiv: https://arxiv.org/abs/2505.01209 · HTML: https://mlsanity.com/paper/2505.01209/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 2 May 2025.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022)
- [SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2308.09361/) (2023)
- [Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications](https://mlsanity.com/paper/2401.00124/) (2023)
- [Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models](https://mlsanity.com/paper/2403.17256/) (2024)
- [High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models](https://mlsanity.com/paper/2309.15889/) (2023)
- [Language-Oriented Semantic Latent Representation for Image Transmission](https://mlsanity.com/paper/2405.09976/) (2024)
- [Evolving Semantic Communication with Generative Model](https://mlsanity.com/paper/2403.20237/) (2024)
- [Retrieval-augmented Generation for GenAI-enabled Semantic Communications](https://mlsanity.com/paper/2412.19494/) (2024)
- [SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models](https://mlsanity.com/paper/2503.12484/) (2025)

## Built on by (most cited first, 2 of 2)

- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Generative Semantic Communication via Alternating Dual-Domain Posterior Sampling](https://mlsanity.com/paper/2604.16796/) (2026)

## Abstract (arXiv)

Semantic communication (SemCom) has recently emerged as a promising paradigm for next-generation wireless systems. Empowered by advanced artificial intelligence (AI) technologies, SemCom has achieved significant improvements in transmission quality and efficiency. However, existing SemCom systems either rely on training over large datasets and specific channel conditions or suffer from performance degradation under channel noise when operating in a training-free manner. To address these issues, we explore the use of generative diffusion models (GDMs) as training-free SemCom systems. Specifically, we design a semantic encoding and decoding method based on the inversion and sampling process of the denoising diffusion implicit model (DDIM), which introduces a two-stage forward diffusion process, split between the transmitter and receiver to enhance robustness against channel noise. Moreover, we optimize sampling steps to compensate for the increased noise level caused by channel noise. We also conduct a brief analysis to provide insights about this design. Simulations on the Kodak dataset validate that the proposed system outperforms the existing baseline SemCom systems across various metrics.