Deep learning scheme for recovery of broadband microwave photonic receiving systems in transceivers without expert knowledge and system priors

TL;DR

This paper presents a deep learning approach that automatically recovers distorted broadband microwave photonic signals in transceivers without requiring expert knowledge or system priors, improving performance in complex systems.

Contribution

A unified deep learning scheme that learns inverse distortions in microwave photonic systems without prior system knowledge, enabling broad applicability and robustness.

Findings

Effective recovery of broadband signals demonstrated in experiments.

Scheme shows robustness to noise and system variations.

Potential for low-cost enhancement of microwave photonic transceivers.

Abstract

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.