  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.
