mlsanityData to 7 Oct 2026

Deep Architectures for Modulation Recognition

arXiv 1703.09197 ↗First posted 27 Mar 2017cs.LGcited by 33

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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.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 5; 2023: 3; 2024: 5; 2025: 5; 2026: 7.

33 papers.

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  1. 2021Transfer Learning for Future Wireless Networks: A Comprehensive Surveycited by 18
  2. 2023AMC-Net: An Effective Network for Automatic Modulation Classificationcited by 9
  3. 2023Multi-task Learning for Radar Signal Characterisationcited by 9
  4. 2021A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networkscited by 7
  5. 2021Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognitioncited by 5
  6. 2022An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classificationcited by 4
  7. 2024Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classificationcited by 4
  8. 2021On the benefits of robust models in modulation recognitioncited by 2
  9. 2021SafeAMC: Adversarial training for robust modulation recognition modelscited by 2
  10. 2025Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classificationcited by 2
  11. 2025AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilitiescited by 2
  12. 2025Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directionscited by 2
21 more
  1. 2021Classification of Common Waveforms Including a Watchdog for Unknown Signalscited by 1
  2. 2021Digital Signal Processing Using Deep Neural Networkscited by 1
  3. 2022RF Signal Transformation and Classification using Deep Neural Networkscited by 1
  4. 2023Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learningcited by 1
  5. 2024Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learningcited by 1
  6. 2025Transmitter Identification and Protocol Categorization in Shared Spectrum via Multi-Task RF Classification at the Network Edgecited by 1
  7. 2026An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognitioncited by 1
  8. 2021Open-set Classification of Common Waveforms Using A Deep Feed-forward Network and Binary Isolation Forest Modelscited by 0
  9. 2022Deep Learning for Spectral Filling in Radio Frequency Applicationscited by 0
  10. 2022Training from Zero: Radio Frequency Machine Learning Data Quantity Forecastingcited by 0
  11. 2022Maximum Likelihood Distillation for Robust Modulation Classificationcited by 0
  12. 2024Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysiscited by 0
  13. 2024Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifierscited by 0
  14. 2024Deep Learning for Low-Latency, Quantum-Ready RF Sensingcited by 0
  15. 2025ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditionscited by 0
  16. 2026Transformer-Based Cognitive Radio: Adaptive Modulation Strategies Using Transformer Modelscited by 0
  17. 2026ZoomSpec: A Physics-Guided Coarse-to-Fine Framework for Wideband Spectrum Sensingcited by 0
  18. 2026TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devicescited by 0
  19. 2026DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classificationcited by 0
  20. 2026A Unified Backbone--Expert Framework with Relation-Token and Residual--Classifier Interfaces for Automatic Modulation Recognitioncited by 0
  21. 2026HFEMCNet: A Compact Hybrid Frequency Enriched Multi Channel Network for Automatic Modulation Classificationcited by 0