# Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach

arXiv: https://arxiv.org/abs/2211.15440 · HTML: https://mlsanity.com/paper/2211.15440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds](https://mlsanity.com/paper/1808.10038/) (2018)
- [Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays](https://mlsanity.com/paper/1902.07678/) (2019)
- [Beam Focusing for Near-Field Multi-User MIMO Communications](https://mlsanity.com/paper/2105.13087/) (2021)
- [Channel Estimation for Extremely Large-Scale Massive MIMO Systems](https://mlsanity.com/paper/1910.05432/) (2019)
- [Near-field Wireless Power Transfer for 6G Internet-of-Everything Mobile Networks: Opportunities and Challenges](https://mlsanity.com/paper/2108.07576/) (2021)
- [Near-Field Spatial Correlation for Extremely Large-Scale Array Communications](https://mlsanity.com/paper/2201.03040/) (2022)

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

- [Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO](https://mlsanity.com/paper/2605.12578/) (2026)

## Abstract (arXiv)

Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising technology for future wireless communications. The deployment of XL-MIMO, especially at high-frequency bands, leads to users being located in the near-field region instead of the conventional far-field. This letter proposes efficient model-based deep learning algorithms for estimating the near-field wireless channel of XL-MIMO communications. In particular, we first formulate the XL-MIMO near-field channel estimation task as a compressed sensing problem using the spatial gridding-based sparsifying dictionary, and then solve the resulting problem by applying the Learning Iterative Shrinkage and Thresholding Algorithm (LISTA). Due to the near-field characteristic, the spatial gridding-based sparsifying dictionary may result in low channel estimation accuracy and a heavy computational burden. To address this issue, we further propose a new sparsifying dictionary learning-LISTA (SDL-LISTA) algorithm that formulates the sparsifying dictionary as a neural network layer and embeds it into LISTA neural network. The numerical results show that our proposed algorithms outperform non-learning benchmark schemes, and SDL-LISTA achieves better performance than LISTA with ten times atoms reduction.