Emami, YousefTaheri, Rahim2020-01-172020-01-172019http://hdl.handle.net/10400.22/153063rd Doctoral Congress in Engineering will be held at FEUP on the 27th to 28th of June, 2019Deep 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.engDeep learningAutoencoderAdversarial autoencoderWhite-box attacksother