# Lightweight Deep Learning Based Channel Estimation for Extremely Large-Scale Massive MIMO Systems

arXiv: https://arxiv.org/abs/2402.08916 · HTML: https://mlsanity.com/paper/2402.08916/

## Key facts

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

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

- [LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimation](https://mlsanity.com/paper/2512.08955/) (2025)

## Abstract (arXiv)

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) systems introduce the much higher channel dimensionality and incur the additional near-field propagation effect, aggravating the computation load and the difficulty to acquire the prior knowledge for channel estimation. In this article, an XL-MIMO channel network (XLCNet) is developed to estimate the high-dimensional channel, which is a universal solution for both the near-field users and far-field users with different channel statistics. Furthermore, a compressed XLCNet (C-XLCNet) is designed via weight pruning and quantization to accelerate the model inference as well as to facilitate the model storage and transmission. Simulation results show the performance superiority and universality of XLCNet. Compared to XLCNet, C-XLCNet incurs the limited performance loss while reducing the computational complexity and model size by about 10 × and 36 ×, respectively.