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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Orbital angular momentum (OAM) at radio-frequency provides a novel approachof multiplexing a set of orthogonal modes on the same frequency channel toachieve high spectral efficiencies. However, classical phase gradient-based OAMmode detection methods require perfect alignment of transmit and receiveantennas, which greatly challenges the practical application of OAMcommunications. In this paper, we first show the effect of non-parallelmisalignment on the OAM phase structure, and then propose the OAM modedetection method based on uniform circular array (UCA) samples learning for themore general alignment or non-parallel misalignment case. Specifically, weapplied three classifiers: K-nearest neighbor (KNN), support vector machine(SVM), and back-propagation neural network (BPNN) to both single-mode andmulti-mode OAM detection. The simulation results validate that the proposedlearning-based OAM mode detection methods are robust to misalignment errors andespecially BPNN classifier has the best generalization performance.

Radio-Frequency Multi-Mode OAM Detection Based on UCA Samples Learning