Deep Learning Based Communication: an Adversarial Approach
Ref: CISTER-TR-190605 Publication Date: 27 to 28, Jun, 2019
Deep Learning Based Communication: an Adversarial Approach
Ref: CISTER-TR-190605 Publication Date: 27 to 28, Jun, 2019Abstract:
Deep learning based communication using autoencoder have revolutionized the design of physical layer in
wireless communication. In this paper, we propose an adversarial autoencoder to mitigate vulnerability of
autoencoder against adversarial attacks. Results confirm the effectiveness of adversarial training by reducing
block error rate (BLER) from 90 percent to 56 percent.
Yousef Emami
Rahim TaheriEvents:
Document:
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DownloadPoster presented in 3rd Doctoral Congress in Engineering (DCE 2019).
Porto, Portugal.
Record Date: 4, Jun, 2019
Short links for this page: www.cister-labs.pt/docs/cister_tr_190605 www.cister-labs.pt/docs/1526