English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In regular microwave photonic (MWP) receiving systems, broadband signals areprocessed in the analog domain before they are transformed to the digitaldomain for further processing and storage. However, the quality of the signalsmay be degraded by defective photonic analog links, especially in a complicatedMWP system. Here, we show a unified deep learning scheme that recovers thedistorted broadband signals as they are transformed to the digital domain. Theneural network could automatically learn the end-to-end inverse responses ofthe distortion effects of actual photonic analog links from data without expertknowledge and system priors. Hence, by shifting or augmenting the datasets, theneural 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 thenoise-robustness of the proposed scheme, showing its superior performance inpractical MWP systems. Therefore, the proposed deep learning scheme facilitatesthe low-cost performance improvement of MWP receiving systems, as well as thenext-generation broadband transceivers, including radars, communications, andmicrowave imaging.

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