Deep learning enhanced Rydberg multifrequency microwave recognition

TL;DR

This paper introduces a deep learning approach to improve Rydberg atom-based multifrequency microwave recognition, effectively reducing noise impact and enabling direct decoding of complex signals in MW sensing and communication.

Contribution

It combines deep learning with Rydberg atom measurements to enhance multifrequency MW recognition without solving complex master equations, advancing MW sensing technology.

Findings

Deep learning reduces noise effects in Rydberg MW sensing.

The system enables direct decoding of frequency-division multiplexed signals.

Improves robustness and accuracy of Rydberg-based MW field recognition.

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.