Identification of communication signals using learning approaches for cognitive radio applications

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dc.contributor.authorXu, Zhengjia
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2020-09-03T15:49:05Z
dc.date.available2020-09-03T15:49:05Z
dc.date.freetoread2020-09-03
dc.date.issued2020-07-14
dc.description.abstractSignal detection, identification, and characterization are among the major challenges in aerial communication systems. The ability to detect and recognize signals using cognitive technologies is still under active development when addressing uncertainties regarding signal parameters, such as blank spaces available within the transmitted signal and the utilized bandwidth. This paper proposes a learning-based identification framework for heterogeneous signals with orthogonal frequency division multiplexing (OFDM) modulation as generated in a simulated environment at an a priori unknown frequency. The implemented region-based signal identification method utilizes cyclostationary features for robust signal detection. Signal characterization is performed using a purposely-built, lightweight, region-based convolutional neural network (R-CNN). It is shown that the proposed framework is robust in the presence of additive white Gaussian noise (AWGN) and, despite its simplicity, shows better performance compared with conventional popular network architectures, such as GoogLeNet, AlexNet, and VGG 16. The signal characterization performance is validated under two degraded environments that are unknown to the system: Doppler shifted and small-scale fading. High performance is demonstrated under both degraded conditions over a wide range of signal to noise ratios (SNRs) and it is shown that the detection probability for the proposed approach is improved over those for conventional energy detectors. It is found that the signal characterization performance deteriorates under extreme conditions, such as lower SNRs and higher Doppler shiftsen_UK
dc.identifier.citationXu Z, Petrunin I, Tsourdos A. (2020) Identification of communication signals using learning approaches for cognitive radio applications. IEEE Access, Volume 8, 2020, pp.128930-128941en_UK
dc.identifier.cris27805836
dc.identifier.issn2169-3536
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3009181
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15749
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectwireless communicationen_UK
dc.subjectsoftware-defined communicationen_UK
dc.subjectregion-based convolu-tional neural networken_UK
dc.subjectcognitive radioen_UK
dc.subjectspectrum sensingen_UK
dc.subjectdeep learningen_UK
dc.subjectBlind detectionen_UK
dc.titleIdentification of communication signals using learning approaches for cognitive radio applicationsen_UK
dc.typeArticleen_UK

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