A support data-based core-set selection method for signal recognition

In recent years, deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment. However, training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs. This paper proposes a support databased core-set selection method (SD) for signal recognition, aiming to screen a representative subset that approximates the large signal dataset. Specifically, this subset can be identified by employing the labeled information during the early stages of model training, as some training samples are labeled as supporting data frequently. This support data is crucial for model training and can be found using a border sample selector. Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size, and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset. The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.