# AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities

arXiv: https://arxiv.org/abs/2502.05315 · HTML: https://mlsanity.com/paper/2502.05315/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [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)
- [Deep Architectures for Modulation Recognition](https://mlsanity.com/paper/1703.09197/) (2017)
- [CodeTF: One-stop Transformer Library for State-of-the-art Code LLMs](https://mlsanity.com/paper/2306.00029/) (2023)
- [Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges](https://mlsanity.com/paper/2207.09647/) (2022)
- [An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation](https://mlsanity.com/paper/2110.04980/) (2021)
- [MEC: Memory-efficient Convolution for Deep Neural Network](https://mlsanity.com/paper/1706.06873/) (2017)
- [Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels](https://mlsanity.com/paper/1911.04970/) (2019)
- [AMC-Net: An Effective Network for Automatic Modulation Classification](https://mlsanity.com/paper/2304.00445/) (2023)
- [Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction](https://mlsanity.com/paper/2401.01056/) (2024)
- [Deep Learning Radio Frequency Signal Classification with Hybrid Images](https://mlsanity.com/paper/2105.09063/) (2021)
- [Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets](https://mlsanity.com/paper/2410.22748/) (2024)

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

- [Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMs](https://mlsanity.com/paper/2505.03112/) (2025)
- [An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition](https://mlsanity.com/paper/2608.00796/) (2026)

## Abstract (arXiv)

We present a review of high-performance automatic modulation recognition (AMR) models proposed in the literature to classify various Radio Frequency (RF) modulation schemes. We replicated these models and compared their performance in terms of accuracy across a range of signal-to-noise ratios. To ensure a fair comparison, we used the same dataset (RadioML-2016A), the same hardware, and a consistent definition of test accuracy as the evaluation metric, thereby providing a benchmark for future AMR studies. The hyperparameters were selected based on the authors' suggestions in the associated references to achieve results as close as possible to the originals. The replicated models are publicly accessible for further analysis of AMR models. We also present the test accuracies of the selected models versus their number of parameters, indicating their complexities. Building on this comparative analysis, we identify strategies to enhance these models' performance. Finally, we present potential opportunities for improvement, whether through novel architectures, data processing techniques, or training strategies, to further advance the capabilities of AMR models.