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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Spectrum sensing is one of the means of utilizing the scarce source ofwireless spectrum efficiently. In this paper, a convolutional neural network(CNN) model employing spectral correlation function which is an effectivecharacterization of cyclostationarity property, is proposed for wirelessspectrum sensing and signal identification. The proposed method classifieswireless signals without a priori information and it is implemented in twodifferent settings entitled CASE1 and CASE2. In CASE1, signals are jointlysensed and classified. In CASE2, sensing and classification are conducted in asequential manner. In contrary to the classical spectrum sensing techniques,the proposed CNN method does not require a statistical decision process anddoes not need to know the distinct features of signals beforehand.Implementation of the method on the measured overthe-air real-world signals incellular bands indicates important performance gains when compared to thesignal classifying deep learning networks available in the literature andagainst classical sensing methods. Even though the implementation herein isover cellular signals, the proposed approach can be extended to the detectionand classification of any signal that exhibits cyclostationary features.Finally, the measurement-based dataset which is utilized to validate the methodis shared for the purposes of reproduction of the results and further researchand development.

Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function