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.