A Novel Framework for Few-Shot RF Fingerprint Identification Using Signal Recurrence Plot and Convolutional Broad Learning Network

Abstract

Radio frequency fingerprint identification (RFFI) is critical for securing Internet of Things (IoT) devices and wireless communication systems. However, existing deep learning approaches often suffer a sharp degradation in accuracy when labeled data is limited. To address this issue, this letter introduces a novel RFFI method, SRP-CBL, which combines signal recurrence plots and convolutional broad learning. It converts RF time series into recurrence plots and applies convolution operations for feature extraction within the broad learning framework. By leveraging sparse connectivity and weight sharing, the model reduces complexity and improves generalization in low-label regimes. Experiments on a public dataset demonstrate that SRP-CBL consistently outperforms state-of-the-art methods in accuracy under limited training data. The dataset can be downloaded from https://cores.ee.ucla.edu/downloads/datasets/wisig/