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

作者：Shaofu Xu, Rui Wang, Xiuting Zou, and Weiwen Zou*
摘要：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 datasets and retraining the same neural network, this algorithm can be applied in various MWP receiving systems. Two MWP receiving systems are setup for experimentally demonstrating the capability of broadband signal recovery. Results evidently show that the neural network can reduce the signal distortion (measured with mean square error) by ~18 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 processer, of which the deployment is of low-cost.
出版源：Journal of the Optical Society of America B
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