Atomic microwave sensing based on CNN-transformer

In recent years, the Rydberg-atom sensor has attracted significant attention due to its high sensitivity, wide dynamic range, and miniaturization capabilities. However, noise from laser power fluctuations, frequency drift, and environment is introduced alongside the intrinsic quantum noise of Rydberg atoms, significantly degrading system performance for microwave sensing. This study tries to reduce the noise impact by combining the Rydberg-atom sensor with the convolutional neural network (CNN)-transformer model. The CNN module captures local temporal features of the signals, while the transformer module extracts global correlations within these features. We apply it to the following classification tasks of atomic microwave sensing: multifrequency microwave recognition, broadband spectrum monitoring, and automatic modulation recognition. We demonstrate that the CNN-transformer model achieves high accuracy across the three tasks and can effectively reduce the noise impact, which holds significant potential for applications in radar detection and wireless communication.