# Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers

arXiv: https://arxiv.org/abs/2510.24495 · HTML: https://mlsanity.com/paper/2510.24495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [MIMO Channel Estimation using Score-Based Generative Models](https://mlsanity.com/paper/2204.07122/) (2022)
- [Generative Diffusion Models for High Dimensional Channel Estimation](https://mlsanity.com/paper/2408.10501/) (2024)
- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits](https://mlsanity.com/paper/2502.06845/) (2025)
- [DiffNMR: Advancing Inpainting of Randomly Sampled Nuclear Magnetic Resonance Signals](https://mlsanity.com/paper/2505.20367/) (2025)
- [Non-Identical Diffusion Models in MIMO-OFDM Channel Generation](https://mlsanity.com/paper/2509.01641/) (2025)

## Built on by (most cited first, 1 of 1)

- [Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G](https://mlsanity.com/paper/2604.06882/) (2026)

## Abstract (arXiv)

With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this context, AI-based channel characterization and estimation become the focus since these methods have not been solved by traditional methods very well and have become the bottleneck of transceiver efficiency in large-scale orthogonal frequency division multiplexing (OFDM) systems. Specifically, by formulating channel estimation as a generative AI problem, generative AI methods such as diffusion models (DMs) can efficiently deal with rough initial estimations and have great potential to cooperate with traditional signal processing methods. This paper focuses on the transceiver design of OFDM systems based on DMs, provides an illustration of the potential of DMs in wireless transceivers, and points out the related research directions brought by DMs. We also provide a proof-of-concept case study of further adapting DMs for better wireless receiver performance.