  We introduce a Channel Distribution Information (CDI)-aware Generative
Adversarial Network (GAN), designed to address the unique challenges of
adversarial attacks in wireless communication systems. The generator in this
CDI-aware GAN maps random input noise to the feature space, generating
perturbations intended to deceive a target modulation classifier. Its
discriminators play a dual role: one enforces that the perturbations follow a
Gaussian distribution, making them indistinguishable from Gaussian noise, while
the other ensures these perturbations account for realistic channel effects and
resemble no-channel perturbations.
  Our proposed CDI-aware GAN can be used as an attacker and a defender. In
attack scenarios, the CDI-aware GAN demonstrates its prowess by generating
robust adversarial perturbations that effectively deceive the target
classifier, outperforming known methods. Furthermore, CDI-aware GAN as a
defender significantly improves the target classifier's resilience against
adversarial attacks.
