Imbalanced Domain Adaptation for Automatic Modulation Classification

Abstract

In non-cooperative communication, complex channel conditions can cause data distribution shift (DDS), which can significantly degrade the performance of existing automatic modulation classification (AMC) models. Initial attempts have explored the use of unsupervised domain adaptation (UDA) to mitigate this issue. However, the considerations for these works are still limited as they assume that the label distribution is domain invariant. In real-world scenarios, significant variations in the usage frequencies of different modulation types often result in imbalanced data volumes and inconsistent label distributions. This challenge, known as label distribution shift (LDS), poses a substantial challenge for cross-domain alignment. To address this limitation, this letter considers AMC in imbalanced domain adaptation (IDA) scenarios, which handle the DDS and LDS simultaneously. We propose a novel method called Pseudo label-based Imbalanced Alignment (PIA). Specifically, our PIA leverages pseudo labels in the target domain to achieve implicit and explicit conditional feature alignment, through re-weighting self-training and centroid feature alignment, respectively. We construct various IDA scenarios for experiments using custom and public datasets, and the results prove the effectiveness and superiority of PIA.