Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs.

Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs.

<p>Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs.</p>

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Bibliographic Details
Main Author:	Jiuxiao Cao (21594403) (author)
Other Authors:	Rui Zhu (305217) (author), Zhen Wang (72451) (author), Jun Wang (5906) (author), Guohao Shi (21594406) (author), Peng Chu (5565407) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified spatial feature diversity sequential learning capacity remote communications due hybrid architecture enhances evaluated using five ensure statistical reliability different receptive fields dataset includes 45 common shortwave protocols captures temporal dependencies 3gale &# 8212 000 labeled samples subcarrier modulations based scale convolutional gru robust signal classification subcarrier classification low signal world sources vital role time deployment study proposes standard gpu selective fading scalable solution results show results confirm range capabilities protocol identification noise ratios multipath propagation inference time gru provides fold cross disaster relief dimensional representations bidirectional gru based real 8 db 10 db
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Summary:	<p>Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs.</p>

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