FATransformer: Feature Alignment Transformer for Unsupervised Domain Adaptation in Radio Frequency Fingerprinting Identification

Abstract— Radio Frequency Fingerprinting Identification (RFFI) serves as a pivotal technology in the Industrial Internet of Things (IIoT), witnessing significant strides over the last decade primarily due to advances in deep learning. However, most existing studies assume that training and test data are independent and identically distributed (i.i.d.), an assumption that often breaks down in real-world IIoT applications, leading to substantial performance degradation in cross-domain scenarios. To address this, we propose FATransformer, a novel unsupervised domain adaptation technique. The method is built on a robust theoretical foundation, ensuring stability and reliability across different domains. Specifically, FATransformer employs a Transformer-based architecture to efficiently process and align intermediate feature maps from both domains. Incorporating an attention-based module, it dynamically adjusts the weights of alignment at various layers, thereby improving the model’s flexibility to handle diverse real-world data. Extensive evaluations across multiple datasets underscore FATransformer’s superiority over existing methods.