Joint Adversarial and Subdomain Adaptation: A Dual-Strategy Framework for Robust Cross-Domain Modulation Recognition
While deep learning has significantly advanced automatic modulation recognition in complex environments, its performance is often limited by domain shifts caused by factors like channel fading and frequency offset. Domain adaptation has emerged as the primary paradigm to address this challenge. However, existing methods face a critical trade-off, as global alignment strategies tend to disrupt class-specific structures, while local alignment methods are overly sensitive to the quality of pseudo-labels. To address this trade-off, this paper proposes a joint adversarial and subdomain adaptation network (JASA-Net) centered on a dual domain adaptation (DDA) strategy. This strategy employs a “global-first, then-local” alignment ap-proach, where an initial global adversarial alignment establishes a strong foundation for generating high-quality pseudo-labels that subsequently guide a local alignment via the local maximum mean discrepancy (LMMD) metric. To extract robust features, we design a patch adaptive multimodal transformer (PAMT) encoder. Furthermore, an efficient “source domain warm-up + aggressive scheduling” training strategy is developed to enhance performance. Extensive experiments on custom-simulated datasets demonstrate that the proposed framework significantly outperforms representative baselines across various cross-domain tasks. Notably, it exhibits remarkable robustness in few-shot scenarios. This work also provides a critical analysis of the task-dependent nature of entropy weighting, offering valuable insights for future research in the field.