# A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2011.08970 · HTML: https://mlsanity.com/paper/2011.08970/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural Network](https://mlsanity.com/paper/2104.12667/) (2021)
- [Optimizing Wireless Networks with Deep Unfolding: Comparative Study on Two Deep Unfolding Mechanisms](https://mlsanity.com/paper/2403.18930/) (2024)

## Abstract (arXiv)

In this paper, we study the usage of Convolutional Neural Network (CNN) estimators for the task of Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Channel Estimation (CE). Specifically, the CNN estimators interpolate the channel values of reference signals for estimating the channel of the full OFDM resource element (RE) matrix. We have designed a 2D CNN architecture based on U-net, and a 3D CNN architecture for handling spatial correlation. We investigate the performance of various CNN architectures fora diverse data set generated according to the 5G NR standard and in particular, we investigate the influence of spatial correlation, Doppler, and reference signal resource allocation. The CE CNN estimators are then integrated with MIMO detection algorithms for testing their influence on the system level Bit Error Rate(BER) performance.