A Learnable Distortion Correction Module for Modulation Recognition

TL;DR

This paper introduces a learnable neural network module that corrects channel-induced distortions in signals, enhancing CNN-based modulation recognition accuracy without needing explicit channel information.

Contribution

It proposes a novel neural distortion correction module that can be trained end-to-end with CNNs for improved modulation recognition in wireless communications.

Findings

Achieves higher accuracy than existing schemes

End-to-end training without explicit channel knowledge

Effective correction of frequency and phase offsets

Abstract

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 \cite{survey}. 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.