Deep learning enhanced Rydberg multifrequency microwave recognition

• URL: http://arxiv.org/abs/2202.13617v1
• Date: Mon, 28 Feb 2022 08:57:47 GMT
• Title: Deep learning enhanced Rydberg multifrequency microwave recognition
• Authors: Zong-Kai Liu, Li-Hua Zhang, Bang Liu, Zheng-Yuan Zhang, Guang-Can Guo, Dong-Sheng Ding, and Bao-Sen Shi
• Abstract summary: Recognition of multifrequency microwave (MW) electric fields is challenging because of the complex interference of multifrequency fields in practical applications. Rydberg atom-based measurements for multifrequency MW electric fields is promising in MW radar and MW communications.
• Score: 8.648875384426352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recognition of multifrequency microwave (MW) electric fields is challenging because of the complex interference of multifrequency fields in practical applications. Rydberg atom-based measurements for multifrequency MW electric fields is promising in MW radar and MW communications. However, Rydberg atoms are sensitive not only to the MW signal but also to noise from atomic collisions and the environment, meaning that solution of the governing Lindblad master equation of light-atom interactions is complicated by the inclusion of noise and high-order terms. Here, we solve these problems by combining Rydberg atoms with deep learning model, demonstrating that this model uses the sensitivity of the Rydberg atoms while also reducing the impact of noise without solving the master equation. As a proof-of-principle demonstration, the deep learning enhanced Rydberg receiver allows direct decoding of the frequency-division multiplexed (FDM) signal. This type of sensing technology is expected to benefit Rydberg-based MW fields sensing and communication.

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