标签稀缺及低信噪比条件下的辐射源驱动频谱状态感知OA

To address the scarcity of spectral data labels and the serious loss of signal-to-noise ratio(SNR)during signal transmission in complex wireless propagation environments,a hybrid attention spectrum sensing network(HA-SenseNet)model is proposed,which combines convolutional neural network(CNN)and Transformer in parallel and introduces the MixMatch semi-supervised learning method to significantly reduce the model's dependence on labeled data by effectively processing local features and global information.Further,in order to help the model focus more on the information-rich regions in the spectral energy image,the efficient spectral attention module(ESAM)is designed in the CNN branch to dynamically adjust the model weights,which effectively solves the problems of visual blurring and feature confusion of the spectral observation image under the low SNR condition.Simulation results show that compared with other classification models and semi-supervised learning methods,HA-SenseNet achieves a significant performance gain through the concatenated architecture,especially in the SNR interval from-20 dB to 5dB with an improvement of 16.8%;in the sparse scenario where the labeled data accounts for 10%of the data,the classification accuracy is close to the level of the fully-supervised learning.