Self-Training Based Adversarial Domain Adaptation for Radio Signal Recognition

Deep learning has made great progress in the field of radio signal recognition. However, existing works usually assume that testing and training data own the same feature domain, which may not be the case in real-world scenarios. The existing cross-domain radio signal recognition methods based on domain adaptation only consider the global feature alignment. A novel unsupervised adversarial domain-adaptive learning framework named DASig is proposed to address the cross-domain radio signal recognition issue for the data acquired in different channel conditions. To align the fine-grained class distributions of the target and source domains, we designed an efficient combined loss function and iterative self-training strategy via target domain pseudo-labels. Moreover, a non-shared domain self-attention mechanism is designed to preserve domain-specific features. Extensive experiment comparisons with five state-of-the-art (SOTA) methods on both the modulation datasets and protocol datasets show the superiority of the DASig.