# EMC2-Net: Joint Equalization and Modulation Classification based on Constellation Network

arXiv: https://arxiv.org/abs/2303.10934 · HTML: https://mlsanity.com/paper/2303.10934/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Fast Deep Learning for Automatic Modulation Classification](https://mlsanity.com/paper/1901.05850/) (2019)
- [Blind Channel Equalization using Variational Autoencoders](https://mlsanity.com/paper/1803.01526/) (2018)
- [Chain-Net: Learning Deep Model for Modulation Classification Under Synthetic Channel Impairment](https://mlsanity.com/paper/2009.02023/) (2020)

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

- [SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation](https://mlsanity.com/paper/2312.05790/) (2023)
- [MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis](https://mlsanity.com/paper/2408.02714/) (2024)

## Abstract (arXiv)

Modulation classification (MC) is the first step performed at the receiver side unless the modulation type is explicitly indicated by the transmitter. Machine learning techniques have been widely used for MC recently. In this paper, we propose a novel MC technique dubbed as Joint Equalization and Modulation Classification based on Constellation Network (EMC2-Net). Unlike prior works that considered the constellation points as an image, the proposed EMC2-Net directly uses a set of 2D constellation points to perform MC. In order to obtain clear and concrete constellation despite multipath fading channels, the proposed EMC2-Net consists of equalizer and classifier having separate and explainable roles via novel three-phase training and noise-curriculum pretraining. Numerical results with linear modulation types under different channel models show that the proposed EMC2-Net achieves the performance of state-of-the-art MC techniques with significantly less complexity.