Deep learning scheme for recovery of broadband microwave photonic receiving systems in transceivers without expert knowledge and system priors
This work addresses signal quality issues in microwave photonic systems for applications like radars and communications, offering a low-cost improvement, though it appears incremental as it applies existing deep learning methods to a new domain.
The authors tackled signal degradation in microwave photonic receiving systems by introducing a deep learning scheme that automatically learns to recover distorted broadband signals without requiring expert knowledge or system priors, achieving superior performance in experiments with complex waveforms.
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.