# SigNet: A Novel Deep Learning Framework for Radio Signal Classification

arXiv: https://arxiv.org/abs/2011.03525 · HTML: https://mlsanity.com/paper/2011.03525/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

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

- [SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models](https://mlsanity.com/paper/2410.14720/) (2024)
- [A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models](https://mlsanity.com/paper/2406.07929/) (2024)
- [Adaptive Visibility Graph Neural Network and its Application in Modulation Classification](https://mlsanity.com/paper/2106.08564/) (2021)
- [Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction](https://mlsanity.com/paper/2401.01056/) (2024)
- [FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition](https://mlsanity.com/paper/2505.21571/) (2025)
- [MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN](https://mlsanity.com/paper/2102.07911/) (2021)
- [Automatic Modulation Classification with Deep Neural Networks](https://mlsanity.com/paper/2301.11773/) (2023)
- [DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning](https://mlsanity.com/paper/2507.12011/) (2025)
- [Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition](https://mlsanity.com/paper/2204.03737/) (2022)
- [MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis](https://mlsanity.com/paper/2408.02714/) (2024)
- [Digital Operating Mode Classification of Real-World Amateur Radio Transmissions](https://mlsanity.com/paper/2501.07337/) (2025)
- [MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition](https://mlsanity.com/paper/2502.19071/) (2025)
- [FoQuS: A Forgetting-Quality Coreset Selection Framework for Automatic Modulation Recognition](https://mlsanity.com/paper/2509.08300/) (2025)
- [ZoomSpec: A Physics-Guided Coarse-to-Fine Framework for Wideband Spectrum Sensing](https://mlsanity.com/paper/2604.13568/) (2026)
- [Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification](https://mlsanity.com/paper/2605.11875/) (2026)

## Abstract (arXiv)

Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, and recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep learning framework called SigNet, where a signal-to-matrix (S2M) operator is adopted to convert the original signal into a square matrix first and is co-trained with a follow-up CNN architecture for classification. This model is further accelerated by integrating 1D convolution operators, leading to the upgraded model SigNet2.0. The simulations on two signal datasets show that both SigNet and SigNet2.0 outperform a number of well-known baselines. More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided. They can achieve a relatively high accuracy even when 1% training data are kept, while other baseline models may lose their effectiveness much more quickly as the datasets get smaller. Such result suggests that SigNet/SigNet2.0 could be extremely useful in the situations where labeled signal data are difficult to obtain. The visualization of the output features of our models demonstrates that our model can well divide different modulation types of signals in the feature hyper-space.