mlsanityData to 14 Oct 2026

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

arXiv 2502.05315 ↗First posted 7 Feb 2025cs.LGcited by 2

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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.

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

15 of 50 references.

References · 15

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2016Tutorial on Variational Autoencoderscited by 409
  3. 2017Over the Air Deep Learning Based Radio Signal Classificationcited by 121
  4. 2016Convolutional Radio Modulation Recognition Networkscited by 106
  5. 2017Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensorscited by 44
  6. 2017Deep Architectures for Modulation Recognitioncited by 33
  7. 2023CodeTF: One-stop Transformer Library for State-of-the-art Code LLMscited by 21
  8. 2022Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challengescited by 16
  9. 2021An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformationcited by 15
  10. 2017MEC: Memory-efficient Convolution for Deep Neural Networkcited by 14
  11. 2019Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channelscited by 14
  12. 2023AMC-Net: An Effective Network for Automatic Modulation Classificationcited by 9
3 more
  1. 2024Enhancing Automatic Modulation Recognition through Robust Global Feature Extractioncited by 7
  2. 2021Deep Learning Radio Frequency Signal Classification with Hybrid Imagescited by 4
  3. 2024Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasetscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMscited by 2
  2. 2026An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognitioncited by 1