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

Different neural network (NN) architectures have different advantages.Convolutional neural networks (CNNs) achieved enormous success in computervision, while recurrent neural networks (RNNs) gained popularity in speechrecognition. It is not known which type of NN architecture is the best fit forclassification of communication signals. In this work, we compare the behaviorof fully-connected NN (FC), CNN, RNN, and bi-directional RNN (BiRNN) in aspectrum sensing task. The four NN architectures are compared on theirdetection performance, requirement of training data, computational complexity,and memory requirement. Given abundant training data and computational andmemory resources, CNN, RNN, and BiRNN are shown to achieve similar performance.The performance of FC is worse than that of the other three types, except inthe case where computational complexity is stringently limited.

Comparison of Neural Network Architectures for Spectrum Sensing