Corpus ID: 237532326

Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications

@article{Kim2021AdversarialAA,
  title={Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications},
  author={Brian Kim and Yi Shi and Yalin Evren Sagduyu and Tugba Erpek and Sennur Ulukus},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.08139}
}
  • Brian Kim, Yi Shi, +2 authors S. Ulukus
  • Published 16 September 2021
  • Computer Science, Engineering, Mathematics
  • ArXiv
We consider adversarial machine learning based attacks on power allocation where the base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a deep neural network (DNN) to serve multiple user equipments (UEs). The DNN that corresponds to a regression model is trained with channel gains as the input and returns transmit powers as the output. While the BS allocates the transmit powers to the UEs to maximize rates for all UEs, there is an adversary that aims to… Expand

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