# Automatic Modulation Classification Using Involution Enabled Residual Networks

arXiv: https://arxiv.org/abs/2108.10001 · HTML: https://mlsanity.com/paper/2108.10001/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2021.

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

- [Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge](https://mlsanity.com/paper/2110.14357/) (2021)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [LIB-KD: Teaching Inductive Bias for Efficient Vision Transformer Distillation and Compression](https://mlsanity.com/paper/2310.00369/) (2023)
- [Spatially Optimized Compact Deep Metric Learning Model for Similarity Search](https://mlsanity.com/paper/2404.06593/) (2024)

## Abstract (arXiv)

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.