# Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information

arXiv: https://arxiv.org/abs/2505.06900 · HTML: https://mlsanity.com/paper/2505.06900/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 11 May 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Understanding Diffusion Models: A Unified Perspective](https://mlsanity.com/paper/2208.11970/) (2022)
- [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022)
- [SinSR: Diffusion-Based Image Super-Resolution in a Single Step](https://mlsanity.com/paper/2311.14760/) (2023)
- [A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications](https://mlsanity.com/paper/2307.07340/) (2023)
- [Integrated Sensing and Communication with Massive MIMO: A Unified Tensor Approach for Channel and Target Parameter Estimation](https://mlsanity.com/paper/2401.01738/) (2024)
- [An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction](https://mlsanity.com/paper/2406.09822/) (2024)
- [Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communications](https://mlsanity.com/paper/2407.12264/) (2024)
- [GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications](https://mlsanity.com/paper/2412.18281/) (2024)
- [FDD Massive MIMO Channel Estimation with Arbitrary 2D-Array Geometry](https://mlsanity.com/paper/1711.06548/) (2017)
- [Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?](https://mlsanity.com/paper/2109.07883/) (2021)
- [High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO System](https://mlsanity.com/paper/2002.06377/) (2020)
- [Near-Field Channel Estimation for Extremely Large-Scale Reconfigurable Intelligent Surface (XL-RIS)-Aided Wideband mmWave Systems](https://mlsanity.com/paper/2304.00440/) (2023)
- [Non-Stationarities in Extra-Large Scale Massive MIMO](https://mlsanity.com/paper/1903.03085/) (2019)
- [Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications](https://mlsanity.com/paper/2212.13527/) (2022)
- [IEEE 802.15.3d: First Standardization Efforts for Sub-Terahertz Band Communications towards 6G](https://mlsanity.com/paper/2011.01683/) (2020)
- [Near-Field Channel Estimation in Mixed LoS/NLoS Environments for Extremely Large-Scale MIMO Systems](https://mlsanity.com/paper/2205.03615/) (2022)
- [Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO](https://mlsanity.com/paper/2307.10837/) (2023)
- [Channel Estimation for XL-MIMO Systems with Polar-Domain Multi-Scale Residual Dense Network](https://mlsanity.com/paper/2308.16400/) (2023)
- [Channel Estimation for Movable-Antenna MIMO Systems Via Tensor Decomposition](https://mlsanity.com/paper/2407.18773/) (2024)
- [Angular-Domain Selective Channel Tracking and Doppler Compensation for High-Mobility mmWave Massive MIMO](https://mlsanity.com/paper/1911.08683/) (2019)
- [Near-Field Channel Modeling for Holographic MIMO Communications](https://mlsanity.com/paper/2403.09411/) (2024)

## Built on by (most cited first, 4 of 4)

- [Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach](https://mlsanity.com/paper/2505.07893/) (2025)
- [EnvCDiff: Joint Refinement of Environmental Information and Channel Fingerprints via Conditional Generative Diffusion Model](https://mlsanity.com/paper/2505.07894/) (2025)
- [RSS map-assisted MIMO channel estimation in the upper mid-band under pilot constraints](https://mlsanity.com/paper/2603.00112/) (2026)
- [CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learning](https://mlsanity.com/paper/2603.25288/) (2026)

## Abstract (arXiv)

This paper investigates the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels. Leveraging the channel's sparsity in the joint AD domain, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimate. Harnessing the powerful implicit prior learning capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel. Specifically, we introduce the preliminary estimated channel as side information, and derive the evidence lower bound (ELBO) of the log-marginal distribution of the target NF channel conditioned on the preliminary estimated channel, which serves as the optimization objective for the proposed generative diffusion model (GDM). Additionally, we introduce a more generalized version of the GDM, the non-Markovian GDM (NM-GDM), to accelerate the sampling process, achieving an approximately tenfold enhancement in sampling efficiency. Experimental results indicate that the proposed approach is capable of offering substantial performance gain in CE compared to existing benchmark schemes within NF XL-MIMO systems. Furthermore, our approach exhibits enhanced generalization capabilities in both the NF or far-field (FF) regions.