# Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification

arXiv: https://arxiv.org/abs/2203.15932 · HTML: https://mlsanity.com/paper/2203.15932/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors](https://mlsanity.com/paper/1707.08908/) (2017)
- [Data Augmentation for Deep Learning-based Radio Modulation Classification](https://mlsanity.com/paper/1912.03026/) (2019)

## Built on by (most cited first, 15 of 15)

- [Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2207.03046/) (2022)
- [RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings](https://mlsanity.com/paper/2501.17888/) (2025)
- [IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G](https://mlsanity.com/paper/2506.06718/) (2025)
- [CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI](https://mlsanity.com/paper/2211.10354/) (2022)
- [Semi-Supervised RF Fingerprinting with Consistency-Based Regularization](https://mlsanity.com/paper/2304.14795/) (2023)
- [Automatic Modulation Classification with Deep Neural Networks](https://mlsanity.com/paper/2301.11773/) (2023)
- [Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning](https://mlsanity.com/paper/2410.20680/) (2024)
- [Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition](https://mlsanity.com/paper/2311.08179/) (2023)
- [MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition](https://mlsanity.com/paper/2502.19071/) (2025)
- [ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions](https://mlsanity.com/paper/2503.18375/) (2025)
- [ModFus-DM: Explore the Representation in Modulated Signal Diffusion Generated Models](https://mlsanity.com/paper/2508.01719/) (2025)
- [Enhancing Automatic Modulation Recognition With a Reconstruction-Driven Vision Transformer Under Limited Labels](https://mlsanity.com/paper/2508.20193/) (2025)
- [Federated Self-Supervised Modulation Classification under Non-IID and Imbalanced Data](https://mlsanity.com/paper/2510.04927/) (2025)
- [Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification](https://mlsanity.com/paper/2605.11875/) (2026)
- [Graph neural networks for sampling-invariant embeddings of organized signal sets](https://mlsanity.com/paper/2609.35934/) (2026)

## Abstract (arXiv)

This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC). By carefully utilizing unlabeled signal data with a self-supervised contrastive-learning pre-training step, our framework achieves higher performance given smaller amounts of labeled data, thereby largely reducing the labeling burden of deep learning. We evaluate the performance of our semi-supervised framework on a public dataset. The evaluation results demonstrate that our semi-supervised approach significantly outperforms supervised frameworks thereby substantially enhancing our ability to train deep neural networks for automatic modulation classification in a manner that leverages unlabeled data.