Adaptive deep-learning algorithm for signal recovery of broadband microwave photonic receiving systems based on supervised training

Abstract

We show an adaptive deep-learning algorithm that recovers the distorted broadband signals of defective microwave photonic (MWP) receiving systems. With data-driven supervised training, the adopted neural network automatically learns the end-to-end distortion effects of the photonic analog links and recovers the received signals in the digital domain. Through changing the training data sets and retraining the same neural network, this algorithm can be applied in various MWP receiving systems. Two MWP receiving systems are set up for experimentally demonstrating the capability of broadband signal recovery. Results show that the neural network can reduce the signal distortion (measured with mean square error) by ${\sim}{18}\;{\rm dB}$. Moreover, visualization analysis indicates that the proposed algorithm is potentially adaptive to more MWP receiving systems and applications. The noise robustness of this algorithm is also verified so that it is applicable in noisy situations. The proposed algorithm improves the performance of MWP receiving systems through appending a deep learning digital processor whose deployment is low cost.

© 2021 Optical Society of America

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