Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments

@article{Shi2019DeepLF,
  title={Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments},
  author={Y. Shi and Kemal Davaslioglu and Y. Sagduyu and W. Headley and Michael Fowler and Gilbert Green},
  journal={2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)},
  year={2019},
  pages={1-10}
}
Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network. We present a deep learning based signal (modulation) classification solution in a realistic wireless network setting, where 1) signal types may change over time; 2) some signal types may be unknown for which there is no training data; 3) signals may be spoofed such as the smart jammers replaying… Expand
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