mlsanityData to 7 Oct 2026

CDDM: Channel Denoising Diffusion Models for Wireless Communications

arXiv 2305.09161 ↗First posted 16 May 2023cs.IT eess.SP math.ITcited by 18

On the map: Diffusion models → Diffusion for compression and wireless

Abstract (arXiv)

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for removing noise leads us to wonder whether DM can be applied to wireless communications to help the receiver eliminate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for wireless communications in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We design corresponding training and sampling algorithms for the forward diffusion process and the reverse sampling process of CDDM. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC). Experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system and the traditional JPEG2000 with low-density parity-check (LDPC) code approach.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models. Builds on 14 of its 20 references that are in the map. First posted on arXiv 16 May 2023.

Builds on

14 of 20 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Denoising Diffusion Error Correction Codes16 Sep

Other references · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2013Auto-Encoding Variational Bayescited by 3,980
  6. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  7. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  8. 2023Video Probabilistic Diffusion Models in Projected Latent Spacecited by 171
  9. 2023A Reparameterized Discrete Diffusion Model for Text Generationcited by 152
  10. 2021What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligencecited by 48
  11. 2022Deep Joint Source-Channel Coding for Semantic Communicationscited by 40
  12. 2018Deep Joint Source-Channel Coding for Wireless Image Transmissioncited by 38
1 more
  1. 2022WITT: A Wireless Image Transmission Transformer for Semantic Communicationscited by 36

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Cited by year

Citing papers per year: 2023: 7; 2024: 7; 2025: 4.

18 papers.

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  1. 2023CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communicationscited by 58
  2. 2024WirelessLLM: Empowering Large Language Models Towards Wireless Intelligencecited by 43
  3. 2024Diffusion-based Generative Prior for Low-Complexity MIMO Channel Estimationcited by 25
  4. 2024Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraintscited by 19
  5. 2023Generative AI for Physical Layer Communications: A Surveycited by 17
  6. 2023CommIN: Semantic Image Communications as an Inverse Problem with INN-Guided Diffusion Modelscited by 14
  7. 2024Diffusion Models as Network Optimizers: Explorations and Analysiscited by 13
  8. 2024DMCE: Diffusion Model Channel Enhancer for Multi-User Semantic Communication Systemscited by 11
  9. 2024Semantic Successive Refinement: A Generative AI-aided Semantic Communication Frameworkcited by 8
  10. 2023Diffusion Models for Wireless Communicationscited by 6
  11. 2025Diffusion Models for Future Networks and Communications: A Comprehensive Surveycited by 6
  12. 2024MOC-RVQ: Multilevel Codebook-Assisted Digital Generative Semantic Communicationcited by 5
6 more
  1. 2023Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approachcited by 4
  2. 2025Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarioscited by 4
  3. 2023Generative AI-Based Probabilistic Constellation Shaping With Diffusion Modelscited by 2
  4. 2025Generative AI Meets Wireless Sensing: Towards Wireless Foundation Modelcited by 1
  5. 2023Denoising Diffusion Probabilistic Models for Hardware-Impaired Communicationscited by 0
  6. 2025CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications
  2. 2025SCDM: Score-Based Channel Denoising Model for Digital Semantic Communications
  3. 2024DM-MIMO: Diffusion Models for Robust Semantic Communications over MIMO Channels
  4. 2025Enabling Training-Free Semantic Communication Systems with Generative Diffusion Models
  5. 2025Joint Source-Channel Noise Adding with Adaptive Denoising for Diffusion-Based Semantic Communications ↗
  6. 2025Denoising and Augmentation: A Dual Use of Diffusion Model for Enhanced CSI Recovery
  7. 2024Diffusion-Aided Joint Source Channel Coding For High Realism Wireless Image Transmission
  8. 2025Conditional Denoising Diffusion for ISAC Enhanced Channel Estimation in Cell-Free 6G