  In regular microwave photonic (MWP) receiving systems, broadband signals are
processed in the analog domain before they are transformed to the digital
domain for further processing and storage. However, the quality of the signals
may be degraded by defective photonic analog links, especially in a complicated
MWP system. Here, we show a unified deep learning scheme that recovers the
distorted broadband signals as they are transformed to the digital domain. The
neural network could automatically learn the end-to-end inverse responses of
the distortion effects of actual photonic analog links from data without expert
knowledge and system priors. Hence, by shifting or augmenting the datasets, the
neural network is potential to be generalized to various MWP receiving systems.
We conduct experiments by nontrivial MWP systems with complicated waveforms.
Results validate the effectiveness, general applicability and the
noise-robustness of the proposed scheme, showing its superior performance in
practical MWP systems. Therefore, the proposed deep learning scheme facilitates
the low-cost performance improvement of MWP receiving systems, as well as the
next-generation broadband transceivers, including radars, communications, and
microwave imaging.
