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Authors
Advisor(s)
Abstract(s)
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.
Description
3rd Doctoral Congress in Engineering will be held at FEUP on the 27th to 28th of June, 2019
Keywords
Deep learning Autoencoder Adversarial autoencoder White-box attacks