Adversarial discriminative domain adaptation for modulation classification based on Ulam stability

Abstract

AbstractDomain adaptation modulation classification aims to improve the cross‐domain robustness of modulation classification, with the basic idea of mitigating the domain shift between the label‐rich source domain and label‐poor target domain in the latent common feature subspace. Due to the complex and heterogeneous wireless propagation conditions, how to construct the optimal latent common feature subspace is a hard problem. In this letter, the authors introduce Ulam stability to character the boundary of the latent common feature space. These theorems can provide guidance for cross‐domain feature extraction and eliminate the domain shift. Based on the proposed stable theorems, the authors define the Ulam loss and present a new method named Adversarial Discriminative Domain Adaptation with Ulam stability (U‐ADDA). Experiments on public datasets prove the effectiveness and generality of the method.