# Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges

arXiv: https://arxiv.org/abs/2207.09647 · HTML: https://mlsanity.com/paper/2207.09647/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2022.

## Built on by (most cited first, 16 of 16)

- [AMC-Net: An Effective Network for Automatic Modulation Classification](https://mlsanity.com/paper/2304.00445/) (2023)
- [Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification](https://mlsanity.com/paper/2408.05151/) (2024)
- [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)
- [A Lightweight Deep Learning Model for Automatic Modulation Classification using Dual Path Deep Residual Shrinkage Network](https://mlsanity.com/paper/2507.04586/) (2025)
- [An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition](https://mlsanity.com/paper/2608.00796/) (2026)
- [Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition](https://mlsanity.com/paper/2311.08179/) (2023)
- [Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack](https://mlsanity.com/paper/2410.01393/) (2024)
- [Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks](https://mlsanity.com/paper/2410.12772/) (2024)
- [Ultralight Signal Classification Model for Automatic Modulation Recognition](https://mlsanity.com/paper/2412.19585/) (2024)
- [Statistical Comparative Analysis of Semantic Similarities and Model Transferability Across Datasets for Short Answer Grading](https://mlsanity.com/paper/2508.15837/) (2025)
- [Large Language Models Can Perform Automatic Modulation Classification via Discretized Self-supervised Candidate Retrieval](https://mlsanity.com/paper/2510.00316/) (2025)
- [Cross-Validated Cross-Channel Self-Attention and Denoising for Automatic Modulation Classification](https://mlsanity.com/paper/2604.10054/) (2026)
- [Edge-Efficient Transformer for End-to-End RF Spectrum Monitoring](https://mlsanity.com/paper/2607.18285/) (2026)
- [Cross-Fitted Residual Utility for Primary-Preserving Cognitive Decision Correction in Automatic Modulation Classification](https://mlsanity.com/paper/2608.02063/) (2026)
- [A Unified Backbone--Expert Framework with Relation-Token and Residual--Classifier Interfaces for Automatic Modulation Recognition](https://mlsanity.com/paper/2608.15160/) (2026)

## Abstract (arXiv)

Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing. Recent breakthroughs in deep learning (DL) have laid the foundation for developing high-performance DL-AMR approaches for communications systems. Comparing with traditional modulation detection methods, DL-AMR approaches have achieved promising performance including high recognition accuracy and low false alarms due to the strong feature extraction and classification abilities of deep neural networks. Despite the promising potential, DL-AMR approaches also bring concerns to complexity and explainability, which affect the practical deployment in wireless communications systems. This paper aims to present a review of the current DL-AMR research, with a focus on appropriate DL models and benchmark datasets. We further provide comprehensive experiments to compare the state of the art models for single-input-single-output (SISO) systems from both accuracy and complexity perspectives, and propose to apply DL-AMR in the new multiple-input-multiple-output (MIMO) scenario with precoding. Finally, existing challenges and possible future research directions are discussed.