# CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systems

arXiv: https://arxiv.org/abs/2503.10297 · HTML: https://mlsanity.com/paper/2503.10297/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 13 Mar 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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [MIMO Channel Estimation using Score-Based Generative Models](https://mlsanity.com/paper/2204.07122/) (2022)
- [Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions](https://mlsanity.com/paper/2308.06250/) (2023)
- [Denoising Diffusion Error Correction Codes](https://mlsanity.com/paper/2209.13533/) (2022)
- [CDDM: Channel Denoising Diffusion Models for Wireless Communications](https://mlsanity.com/paper/2305.09161/) (2023)
- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [Diffusion Models as Network Optimizers: Explorations and Analysis](https://mlsanity.com/paper/2411.00453/) (2024)
- [Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications](https://mlsanity.com/paper/2310.19460/) (2023)
- [Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2407.15784/) (2024)

## Abstract (arXiv)

Generative models, including denoising diffusion models (DM), are gaining attention in wireless applications due to their ability to learn complex data distributions. In this paper, we propose CoDiPhy, a novel framework that leverages conditional denoising diffusion models to address a wide range of wireless physical layer problems. A key challenge of using DM is the need to assume or approximate Gaussian signal models. CoDiPhy addresses this by incorporating a conditional encoder as a guidance mechanism, mapping problem observations to a latent space and removing the Gaussian constraint. By combining conditional encoding, time embedding layers, and a U-Net-based main neural network, CoDiPhy introduces a noise prediction neural network, replacing the conventional approach used in DM. This adaptation enables CoDiPhy to serve as an effective solution for a wide range of detection, estimation, and predistortion tasks. We demonstrate CoDiPhy's adaptability through two case studies: an OFDM receiver for detection and phase noise compensation for estimation. In both cases, CoDiPhy outperforms conventional methods by a significant margin.