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Convolutional Radio Modulation Recognition Networks

arXiv 1602.04105 ↗First posted 12 Feb 2016cs.LG cs.CVcited by 106

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Abstract (arXiv)

We study the adaptation of convolutional neural networks to the complex temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert features which are widely used in the field today and we show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.

Key facts. Cited by 106 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2016.

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Citing papers per year: 2021: 19; 2022: 17; 2023: 19; 2024: 22; 2025: 18; 2026: 11.

106 papers.

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  1. 2024A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Modelscited by 18
  2. 2022Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Predictioncited by 17
  3. 2021An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformationcited by 15
  4. 2022A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challengescited by 15
  5. 2022Self-Contrastive Learning based Semi-Supervised Radio Modulation Classificationcited by 15
  6. 2023Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issuescited by 12
  7. 20256G WavesFM: A Foundation Model for Sensing, Communication, and Localizationcited by 11
  8. 2021Adaptive Visibility Graph Neural Network and its Application in Modulation Classificationcited by 9
  9. 2022Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compressioncited by 9
  10. 2022Robust Bayesian Learning for Reliable Wireless AI: Framework and Applicationscited by 9
  11. 2023AMC-Net: An Effective Network for Automatic Modulation Classificationcited by 9
  12. 2023Multi-task Learning for Radar Signal Characterisationcited by 9
38 more
  1. 2022Machine Learning in NextG Networks via Generative Adversarial Networkscited by 8
  2. 2021A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networkscited by 7
  3. 2024Enhancing Automatic Modulation Recognition through Robust Global Feature Extractioncited by 7
  4. 2021Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classificationcited by 6
  5. 2025SpectrumFM: A Foundation Model for Intelligent Spectrum Managementcited by 6
  6. 2021Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognitioncited by 5
  7. 2025RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammingscited by 5
  8. 2021Deep Learning Radio Frequency Signal Classification with Hybrid Imagescited by 4
  9. 2022An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classificationcited by 4
  10. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  11. 2024Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classificationcited by 4
  12. 2025FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognitioncited by 4
  13. 2021Gradient-based Adversarial Deep Modulation Classification with Data-driven Subsamplingcited by 3
  14. 2021Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edgecited by 3
  15. 2022Towards an AI-Driven Universal Anti-Jamming Solution with Convolutional Interference Cancellation Networkcited by 3
  16. 2023Semi-Supervised RF Fingerprinting with Consistency-Based Regularizationcited by 3
  17. 2023MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Expertscited by 3
  18. 2024Joint Signal Detection and Automatic Modulation Classification via Deep Learningcited by 3
  19. 2025EMind: A Foundation Model for Multi-task Electromagnetic Signals Understandingcited by 3
  20. 2021Blind Modulation Classification via Combined Machine Learning and Signal Feature Extractioncited by 2
  21. 2021On the benefits of robust models in modulation recognitioncited by 2
  22. 2021CLPVG: Circular limited penetrable visibility graph as a new network model for time seriescited by 2
  23. 2021SafeAMC: Adversarial training for robust modulation recognition modelscited by 2
  24. 2022RF Signal Classification with Synthetic Training Data and its Real-World Performancecited by 2
  25. 2023Automatic Modulation Classification with Deep Neural Networkscited by 2
  26. 2023EMC2-Net: Joint Equalization and Modulation Classification based on Constellation Networkcited by 2
  27. 2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2
  28. 2024Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challengescited by 2
  29. 2025AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilitiescited by 2
  30. 2025Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directionscited by 2
  31. 2025Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devicescited by 2
  32. 2025DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learningcited by 2
  33. 2025Adapt under Attack and Domain Shift: Unified Adversarial Meta-Learning and Domain Adaptation for Robust Automatic Modulation Classificationcited by 2
  34. 2022RF Signal Transformation and Classification using Deep Neural Networkscited by 1
  35. 2022TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networkscited by 1
  36. 2023Keep It Simple: CNN Model Complexity Studies for Interference Classification Taskscited by 1
  37. 2023Modulation Classification Through Deep Learning Using Resolution Transformed Spectrogramscited by 1
  38. 2023Using Early Exits for Fast Inference in Automatic Modulation Classificationcited by 1

50 most cited of 106 All 106 (JSON)