A Novel Physical Layer Key Generation Method Based on WGAN-GP Adversarial Autoencoder

The reciprocity of wireless channel between legitimate nodes is the premise of physical layer security key generation. Deep neural networks provide the non-linear transformation method to extract symmetric keys from reciprocal channel responses and show better freedom and performance than traditional methods. However, the hidden layer output in neural networks is unpredictable and the extracted high-dimensional features can not be estimated in advance, which is difficult to be applied to the key generation in the physical layer. In this paper, the key generation method based on WGAN-GP adversarial autoencoder is proposed to extract features efficiently between legitimate nodes, and the features can be fitted to the Gaussian distribution. Meanwhile, the Wasserstein distance and gradient penalty are applied in the game training to solve the gradient explosion. Compared with the PCA method, the proposed method has higher security key capacity, lower key error rate, and no interaction in the key generation process, which avoids key leakage.