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

Non-cooperative communications using non-orthogonal multicarrier signals arechallenging since self-created inter carrier interference (ICI) exists, whichwould prevent successful signal classification. Deep learning (DL) can dealwith the classification task without domain-knowledge at the cost of trainingcomplexity since neural network hyperparameters have to be extensively tuned.Previous work showed that a tremendously trained convolutional neural network(CNN) classifier can efficiently identify feature-diversity dominant signalswhile it failed when feature-similarity dominates. Therefore, a pre-processingstrategy, which can amplify signal feature diversity is of great importance.This work applies single-level wavelet transform to manually extracttime-frequency features from non-orthogonal signals. Composite statisticalfeatures are investigated and the wavelet enabled two-dimensionaltime-frequency feature grid is further simplified into a one-dimensionalfeature vector via proper statistical transform. The dimensionality reducedfeatures are fed to an error-correcting output codes (ECOC) model, consistingof multiple binary support vector machine (SVM) learners, for multiclass signalclassification. Low-cost experiments reveal 100% classification accuracy forfeature-diversity dominant signals and 90% for feature-similarity dominantsignals, which is nearly 28% accuracy improvement when compared with the CNNclassification results.

Wavelet Classification for Over-the-Air Non-Orthogonal Waveforms