# Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems

arXiv: https://arxiv.org/abs/2003.08980 · HTML: https://mlsanity.com/paper/2003.08980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2020.

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

- [A Survey of Applied Machine Learning Techniques for Optical OFDM based Networks](https://mlsanity.com/paper/2105.03289/) (2021)
- [Continual Learning-Aided Super-Resolution Scheme for Channel Reconstruction and Generalization in OFDM Systems](https://mlsanity.com/paper/2503.01897/) (2025)

## Abstract (arXiv)

In this paper, we present a downlink pilot design scheme for Deep Learning (DL) based channel estimation (ChannelNet) in orthogonal frequency-division multiplexing (OFDM) systems. Specifically, in the proposed scheme, a feature selection method named Concrete Autoencoder (ConcreteAE) is used to find the most informative locations for pilot transmission. This autoencoder consists of a concrete layer as the encoder and a multilayer perceptron (MLP) as the decoder. During the training, the concrete layer selects the most informative pilot locations, and the decoder reconstructs an approximate estimation of the channel. Eventually, the ChannelNet is trained on the output of the ConcreteAE aiming to reconstruct the ideal channel response. The estimation error results show that this approach outperforms the previously presented ChannelNet with a uniformly distributed pilot pattern, and its performance is comparable to the minimum mean square error (MMSE).