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

We show that compact fully connected (FC) deep learning networks trained toclassify wireless protocols using a hierarchy of multiple denoisingautoencoders (AEs) outperform reference FC networks trained in a typical way,i.e., with a stochastic gradient based optimization of a given FC architecture.Not only is the complexity of such FC network, measured in number of trainableparameters and scalar multiplications, much lower than the reference FC andresidual models, its accuracy also outperforms both models for nearly alltested SNR values (0 dB to 50dB). Such AE-trained networks are suited forin-situ protocol inference performed by simple mobile devices based on noisysignal measurements. Training is based on the data transmitted by real devices,and collected in a controlled environment, and systematically augmented by apolicy-based data synthesis process by adding to the signal any subset ofimpairments commonly seen in a wireless receiver.

AutoEncoders for Training Compact Deep Learning RF Classifiers for Wireless Protocols