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

Subsampling of received wireless signals is important for relaxing hardwarerequirements as well as the computational cost of signal processing algorithmsthat rely on the output samples. We propose a subsampling technique tofacilitate the use of deep learning for automatic modulation classification inwireless communication systems. Unlike traditional approaches that rely onpre-designed strategies that are solely based on expert knowledge, the proposeddata-driven subsampling strategy employs deep neural network architectures tosimulate the effect of removing candidate combinations of samples from eachtraining input vector, in a manner inspired by how wrapper feature selectionmodels work. The subsampled data is then processed by another deep learningclassifier that recognizes each of the considered 10 modulation types. We showthat the proposed subsampling strategy not only introduces drastic reduction inthe classifier training time, but can also improve the classification accuracyto higher levels than those reached before for the considered dataset. Animportant feature herein is exploiting the transferability property of deepneural networks to avoid retraining the wrapper models and obtain superiorperformance through an ensemble of wrappers over that possible through solelyrelying on any of them.

Ensemble Wrapper Subsampling for Deep Modulation Classification