Automatic Modulation Classification Using Involution Enabled Residual Networks

TL;DR

This paper introduces an involution-enabled residual network for automatic modulation classification, significantly improving accuracy and speed over traditional CNNs, suitable for next-generation wireless networks.

Contribution

It proposes a novel involution-based residual network architecture that enhances discrimination and efficiency for AMC tasks, addressing high computational costs of existing models.

Findings

Achieves superior classification accuracy

Demonstrates faster convergence speed

Outperforms benchmark schemes

Abstract

Automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications. Many deep learning based models especially convolution neural networks (CNNs) have been proposed for AMC. However, the computation cost is very high, which makes them inappropriate for beyond the fifth generation wireless communication networks that have stringent requirements on the classification accuracy and computing time. In order to tackle those challenges, a novel involution enabled AMC scheme is proposed by using the bottleneck structure of the residual networks. Involution is utilized instead of convolution to enhance the discrimination capability and expressiveness of the model by incorporating a self-attention mechanism. Simulation results demonstrate that our proposed scheme achieves superior classification performance and faster convergence speed comparing with other benchmark schemes.