# A Learnable Distortion Correction Module for Modulation Recognition

arXiv: https://arxiv.org/abs/1803.01319 · HTML: https://mlsanity.com/paper/1803.01319/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation](https://mlsanity.com/paper/2110.04980/) (2021)
- [Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition](https://mlsanity.com/paper/2111.03258/) (2021)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification](https://mlsanity.com/paper/2106.10543/) (2021)

## Abstract (arXiv)

Modulation recognition is a challenging task while performing spectrum sensing in a cognitive radio setup. Recently, the use of deep convolutional neural networks (CNNs) has shown to achieve state-of-the-art accuracy for modulation recognition citesurvey. However, a wireless channel distorts the signal and CNNs are not explicitly designed to undo these artifacts. To improve the performance of CNN-based recognition schemes we propose a signal distortion correction module (CM) and show that this CM+CNN scheme achieves accuracy better than the existing schemes. The proposed CM is also based on a neural network that estimates the random carrier frequency and phase offset introduced by the channel and feeds it to a part that undoes this distortion right before CNN-based modulation recognition. Its output is differentiable with respect to its weights, which allows it to be trained end-to-end with the modulation recognition CNN based on the received signal. For supervision, only the modulation scheme label is used and the knowledge of true frequency or phase offset is not required.