Modulation-Agnostic Spectrum Sensing based on Anomaly detection for Cognitive Radio

This work proposes a Spectrum Sensing (SS) scheme based on a Convolutional Autoencoder (CAE) for application in Cognitive Radio Networks. The channel occupancy is modeled as an anomaly detection problem. The CAE is trained only with noise-signal samples so that any random modulated signal observed i...

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Main Authors: Cesar Pablos, Ángel G. Andrade, Guillermo Galaviz
Format: Article
Language:English
Published: Elsevier 2023-06-01
Series:ICT Express
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2405959522000480
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author Cesar Pablos
Ángel G. Andrade
Guillermo Galaviz
author_facet Cesar Pablos
Ángel G. Andrade
Guillermo Galaviz
author_sort Cesar Pablos
collection DOAJ
description This work proposes a Spectrum Sensing (SS) scheme based on a Convolutional Autoencoder (CAE) for application in Cognitive Radio Networks. The channel occupancy is modeled as an anomaly detection problem. The CAE is trained only with noise-signal samples so that any random modulated signal observed in the wireless channel will be regarded as an outlier. The relationship between the detection threshold and the system performance is evaluated. Experiments demonstrate that the SS proposal can identify primary signal presence with better accuracy (high detection and low false alarm rate) than conventional Energy Detection.
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spelling doaj.art-7485d69428484c3aa5d819868c51d2432023-06-25T04:43:17ZengElsevierICT Express2405-95952023-06-0193398402Modulation-Agnostic Spectrum Sensing based on Anomaly detection for Cognitive RadioCesar Pablos0Ángel G. Andrade1Guillermo Galaviz2Facultad de Ingeniería, Universidad Autónoma de Baja California, Blvd. Benito Juarez s/n, Mexicali, Baja California, 21280, MéxicoCorresponding author.; Facultad de Ingeniería, Universidad Autónoma de Baja California, Blvd. Benito Juarez s/n, Mexicali, Baja California, 21280, MéxicoFacultad de Ingeniería, Universidad Autónoma de Baja California, Blvd. Benito Juarez s/n, Mexicali, Baja California, 21280, MéxicoThis work proposes a Spectrum Sensing (SS) scheme based on a Convolutional Autoencoder (CAE) for application in Cognitive Radio Networks. The channel occupancy is modeled as an anomaly detection problem. The CAE is trained only with noise-signal samples so that any random modulated signal observed in the wireless channel will be regarded as an outlier. The relationship between the detection threshold and the system performance is evaluated. Experiments demonstrate that the SS proposal can identify primary signal presence with better accuracy (high detection and low false alarm rate) than conventional Energy Detection.http://www.sciencedirect.com/science/article/pii/S2405959522000480Anomaly detectionCognitive radioConvolutional autoencoderSpectrum sensing
spellingShingle Cesar Pablos
Ángel G. Andrade
Guillermo Galaviz
Modulation-Agnostic Spectrum Sensing based on Anomaly detection for Cognitive Radio
ICT Express
Anomaly detection
Cognitive radio
Convolutional autoencoder
Spectrum sensing
title Modulation-Agnostic Spectrum Sensing based on Anomaly detection for Cognitive Radio
title_full Modulation-Agnostic Spectrum Sensing based on Anomaly detection for Cognitive Radio
title_fullStr Modulation-Agnostic Spectrum Sensing based on Anomaly detection for Cognitive Radio
title_full_unstemmed Modulation-Agnostic Spectrum Sensing based on Anomaly detection for Cognitive Radio
title_short Modulation-Agnostic Spectrum Sensing based on Anomaly detection for Cognitive Radio
title_sort modulation agnostic spectrum sensing based on anomaly detection for cognitive radio
topic Anomaly detection
Cognitive radio
Convolutional autoencoder
Spectrum sensing
url http://www.sciencedirect.com/science/article/pii/S2405959522000480
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