Wireless Spectrum Status Sensing Driven by Few-Shot Learning

Wireless spectrum status sensing is one of the prerequisites for achieving efficient utilization of spectrum resources and harmonious coexistence among systems. A spectrum sensing scheme based on interpolation and Few-Shot Learning(FSL) classification is proposed to address the sparsity of spectrum data, unstable distribution of data categories, and severe shortage of labeled data in complex wireless propagation environments. Firstly, the sparsely distributed observation data is interpolated and a spectral status map is constructed as the input data to the spectral status classifier. Then, for the cases where the distributions of data categories are unstable and the amount of data is severely insufficient, a few-shot learning-based classification algorithm is proposed, incorporating the embedding modules and measurement modules to realize fast and accurate spectrum status classification. Specifically, the embedding module is used to map spectral data to the embedding space and extract hidden image features from the spectral data. In the measurement module, two category representation methods, prototype-based and sample-based, are proposed to determine the category of the samples by calculating the similarity between the samples and the categories. Finally, an A-way B-shot task training model is set to ensure that the classification model will not cause overfitting problems due to the small number of test samples. Simulation results show that compared with traditional machine learning methods, the proposed model can achieve accurate classification under low signal-to-noise ratio conditions. In addition, it can quickly distinguish the categories of radiation source activity scenarios even when the number of samples in the test set is small or when new classes that have never been seen in the training set appear in the test set. © 2024 Science Press. All rights reserved.