# Deep Architectures for Modulation Recognition

arXiv: https://arxiv.org/abs/1703.09197 · HTML: https://mlsanity.com/paper/1703.09197/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2017.

## Built on by (most cited first, 30 of 33)

- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [Multi-task Learning for Radar Signal Characterisation](https://mlsanity.com/paper/2306.13105/) (2023)
- [AMC-Net: An Effective Network for Automatic Modulation Classification](https://mlsanity.com/paper/2304.00445/) (2023)
- [A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networks](https://mlsanity.com/paper/2105.15037/) (2021)
- [Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition](https://mlsanity.com/paper/2111.03258/) (2021)
- [An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification](https://mlsanity.com/paper/2202.09777/) (2022)
- [Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification](https://mlsanity.com/paper/2408.05151/) (2024)
- [On the benefits of robust models in modulation recognition](https://mlsanity.com/paper/2103.14977/) (2021)
- [SafeAMC: Adversarial training for robust modulation recognition models](https://mlsanity.com/paper/2105.13746/) (2021)
- [Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification](https://mlsanity.com/paper/2501.01620/) (2025)
- [AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities](https://mlsanity.com/paper/2502.05315/) (2025)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [Classification of Common Waveforms Including a Watchdog for Unknown Signals](https://mlsanity.com/paper/2108.07339/) (2021)
- [Digital Signal Processing Using Deep Neural Networks](https://mlsanity.com/paper/2109.10404/) (2021)
- [RF Signal Transformation and Classification using Deep Neural Networks](https://mlsanity.com/paper/2204.03564/) (2022)
- [Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning](https://mlsanity.com/paper/2312.16715/) (2023)
- [Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning](https://mlsanity.com/paper/2412.21164/) (2024)
- [Transmitter Identification and Protocol Categorization in Shared Spectrum via Multi-Task RF Classification at the Network Edge](https://mlsanity.com/paper/2511.01198/) (2025)
- [An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition](https://mlsanity.com/paper/2608.00796/) (2026)
- [Open-set Classification of Common Waveforms Using A Deep Feed-forward Network and Binary Isolation Forest Models](https://mlsanity.com/paper/2110.00252/) (2021)
- [Deep Learning for Spectral Filling in Radio Frequency Applications](https://mlsanity.com/paper/2204.01536/) (2022)
- [Training from Zero: Radio Frequency Machine Learning Data Quantity Forecasting](https://mlsanity.com/paper/2205.03703/) (2022)
- [Maximum Likelihood Distillation for Robust Modulation Classification](https://mlsanity.com/paper/2211.00748/) (2022)
- [Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis](https://mlsanity.com/paper/2404.15343/) (2024)
- [Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers](https://mlsanity.com/paper/2404.15344/) (2024)
- [Deep Learning for Low-Latency, Quantum-Ready RF Sensing](https://mlsanity.com/paper/2404.17962/) (2024)
- [ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions](https://mlsanity.com/paper/2503.18375/) (2025)
- [Transformer-Based Cognitive Radio: Adaptive Modulation Strategies Using Transformer Models](https://mlsanity.com/paper/2601.10519/) (2026)
- [ZoomSpec: A Physics-Guided Coarse-to-Fine Framework for Wideband Spectrum Sensing](https://mlsanity.com/paper/2604.13568/) (2026)
- [TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devices](https://mlsanity.com/paper/2605.15656/) (2026)

## Abstract (arXiv)

We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods.