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Automatic Modulation Classification under IQ Imbalance Using Supervised Learning.

, , and . MILCOM, page 1622-1627. IEEE, (2013)

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Other publications of authors with the same name

On the Limitations of Targeted Adversarial Evasion Attacks Against Deep Learning Enabled Modulation Recognition., , , , and . WiseML@WiSec, page 25-30. ACM, (2019)Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments., , , , , and . DySPAN, page 1-10. IEEE, (2019)When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions., , , , , , and . CoRR, (2020)Developing RFML Intuition: An Automatic Modulation Classification Architecture Case Study., , , , , , , and . MILCOM, page 292-298. IEEE, (2019)Investigating a spectral deception loss metric for training machine learning-based evasion attacks., , and . WiseML@WiSec, page 43-48. ACM, (2020)Training Data Augmentation for Deep Learning RF Systems., , , and . CoRR, (2020)Exploiting Radio Correlation and Reliability Information in Collaborative Spectrum Sensing., , and . IEEE Communications Letters, 15 (8): 825-827 (2011)Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications., , and . CoRR, (2019)Deep Reinforcement Learning for Dynamic Spectrum Access in Wireless Networks., , , and . MILCOM, page 207-212. IEEE, (2018)Enhanced PHY-layer security via co-channel underlays., , , and . IPCCC, page 1-6. IEEE Computer Society, (2016)