# Deep Learning-Based CSI Feedback for XL-MIMO Systems in the Near-Field Domain

arXiv: https://arxiv.org/abs/2405.09053 · HTML: https://mlsanity.com/paper/2405.09053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2024.

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

- [Learnware for CSI Feedback: Scene-specific Small Models Can Do Big](https://mlsanity.com/paper/2608.17760/) (2026)

## Abstract (arXiv)

In this paper, we consider an extremely large-scale massive multiple-input-multiple-output (XL-MIMO) system. As the scale of antenna arrays increases, the range of near-field communications also expands. In this case, the signals no longer exhibit planar wave characteristics but spherical wave characteristics in the near-field channel, which makes the channel state information (CSI) highly complex. Additionally, the increase of the antenna arrays scale also makes the size of the CSI matrix significantly increase. Therefore, CSI feedback in the near-field channel becomes highly challenging. To solve this issue, we propose a deep-learning (DL)-based ExtendNLNet that can compress the CSI, and further reduce the overhead of CSI feedback. In addition, we have introduced the Non-Local block to obtain a larger area of CSI features. Simulation results show that the proposed ExtendNLNet can significantly improve the CSI recovery quality compared to other DL-based methods.