# Near-Field Wideband Channel Estimation for XL-MIMO Systems via Denoising Diffusion Model

arXiv: https://arxiv.org/abs/2604.20494 · HTML: https://mlsanity.com/paper/2604.20494/

## 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 18 of its 42 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Placed in

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

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems](https://mlsanity.com/paper/2211.12343/) (2022)
- [On the Asymptotic Mean Square Error Optimality of Diffusion Models](https://mlsanity.com/paper/2403.02957/) (2024)
- [Beam Focusing for Near-Field Multi-User MIMO Communications](https://mlsanity.com/paper/2105.13087/) (2021)
- [Next Generation Advanced Transceiver Technologies for 6G and Beyond](https://mlsanity.com/paper/2403.16458/) (2024)
- [An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation](https://mlsanity.com/paper/2211.15939/) (2022)
- [Near-Field Wideband Beamforming for Extremely Large Antenna Arrays](https://mlsanity.com/paper/2109.10054/) (2021)
- [Extremely Large-Scale MIMO: Fundamentals, Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2209.12131/) (2022)
- [Near-Field Channel Estimation for Extremely Large-Scale Reconfigurable Intelligent Surface (XL-RIS)-Aided Wideband mmWave Systems](https://mlsanity.com/paper/2304.00440/) (2023)
- [Wideband Channel Estimation for THz Massive MIMO](https://mlsanity.com/paper/2108.08173/) (2021)
- [Near-Field Wideband Channel Estimation for Extremely Large-Scale MIMO](https://mlsanity.com/paper/2212.08401/) (2022)
- [Beam Squint Assisted User Localization in Near-Field Integrated Sensing and Communications Systems](https://mlsanity.com/paper/2309.14012/) (2023)
- [Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect](https://mlsanity.com/paper/2403.12813/) (2024)
- [Near-Field Wideband Beam Training Based on Distance-Dependent Beam Split](https://mlsanity.com/paper/2406.07989/) (2024)
- [Low-Complexity On-Grid Channel Estimation for Partially-Connected Hybrid XL-MIMO](https://mlsanity.com/paper/2502.09929/) (2025)
- [Near-Field ISAC for THz Wireless Systems](https://mlsanity.com/paper/2507.04292/) (2025)
- [NEFT: A Unified Transformer Framework for Efficient Near-Field CSI Feedback in XL-MIMO Systems](https://mlsanity.com/paper/2509.12748/) (2025)

## Abstract (arXiv)

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key enabling technology for sixth-generation (6G) communication systems. Nevertheless, the increase in array aperture and signal bandwidth brings new challenges to wideband channel estimation in XL-MIMO systems. Motivated by recent advances in deep generative modeling, we propose a diffusion model-based method for near-field wideband channel estimation in XL-MIMO systems. We first analyze the statistical correlation of wideband channel and show that near-field wideband channel exhibits both spatial non-stationarity and beam split effects. Based on these observations, the channel estimation problem is formulated as a Bayesian posterior inference task, in which a diffusion model is employed to learn the prior distribution of the channel. To further enhance the representation of complex spatial-frequency channel structures, we design a denoising network with a multi-scale attention mechanism. In particular, the network extracts multi-scale spatial-frequency features via parallel convolutional branches with different receptive fields, and combines feature attention and spatial attention modules to adaptively emphasize critical channel features. This design enables more accurate modeling of near-field wideband channel distributions and consequently improves channel estimation performance. Experimental results demonstrate that the proposed method exhibits superior robustness to existing baseline schemes for XL-MIMO wideband channel estimation under different experimental settings.