Learning based spectrum hole detection for cognitive radio communication

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dc.contributor.authorXu, Zhengjia
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorAyub, Shahid
dc.date.accessioned2021-03-22T13:44:43Z
dc.date.available2021-03-22T13:44:43Z
dc.date.issued2020-04-30
dc.description.abstractThis paper proposes a novel learning based (LB) solution for detection and quantification of spectrum holes in periodic communications of unmanned aerial vehicles (UAVs), Instead of hypothesis testing after implementation of spectrum sensing methods, the implemented LB solution based on spectral correlation function (SCF) uses region convolutional neural network (R-CNN) for extracting quantitative parameters of the spectrum holes. The proposed LB approach is implemented using GoogLeNet architecture for the wide band detection in the scenario of orthogonal frequency division multiplexing (OFDM) communication system with the additive white Gaussian noise (AWGN) channel model. The simulation of single input single output (SISO) communication system with spectrum holes is presented. Examples of wide band detection results for both SISO and multiple input multiple output (MIMO) systems are shown and the proposed LB detector is found to be fairly accurate in identification of spectrum holes. By analyzing the training performance, the GoogLeNet architecture, along with its hyperparameter configurations and training dataset is validated. We also demonstrated that our LB detector is resilient to the AWGN environment by analyzing the precision and recall curves, average precision and mean relative error (MRE) versus signal noise ratio (SNR).en_UK
dc.identifier.citationXu Z, Petrunin I, Tsourdos A, Ayub S. (2020) Learning based spectrum hole detection for cognitive radio communication. In: 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), 8-12 September 2019, San Diego, CA, USAen_UK
dc.identifier.eisbn978-1-7281-0649-6
dc.identifier.eissn2155-7209
dc.identifier.isbn978-1-7281-1497-2
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/DASC43569.2019.9081799
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16488
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectUAV communicationen_UK
dc.subjectcyclostationary feature sensingen_UK
dc.subjectR-CNNen_UK
dc.subjectcognitive radioen_UK
dc.subjectlearning based sensingen_UK
dc.titleLearning based spectrum hole detection for cognitive radio communicationen_UK
dc.typeConference paperen_UK

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