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AuthorAlmohamad A.
AuthorHasna , Mazen
AuthorAlthunibat S.
AuthorTekbiyik K.
AuthorQaraqe K.
Available date2022-04-26T11:06:45Z
Publication Date2021
Publication NameInternational Conference on ICT Convergence
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICTC52510.2021.9621015
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85122945783&doi=10.1109%2fICTC52510.2021.9621015&partnerID=40&md5=fdf655733fafa83e8dba26ad2cb35699
URIhttp://hdl.handle.net/10576/30442
AbstractWith the witnessed exponential growth of Internet of Things (IoT) nodes deployment following the emerging applications, multiple variants of technologies have been proposed to handle the IoT requirements. Among the proposed technologies, LoRa stands as a promising solution thanks to its tiny footprint in terms of cost and power consumption. Since the ISM band is usually used for such applications and multiple different systems are allocated in this band, a smart spectrum management and awareness is highly required. In this paper, we propose a convolutional neural network (CNN)-based classifier to identify LoRa spreading factors (SF) and the inter-SF interference. Specifically, in the proposed model LoRa signals are pre-processed using spectral correlation function (SCF) and fast Fourier transform (FFT). We show that using the SCF pre-processed signals for training can attain a better performance as compared to those with FFT pre-processed training data in terms of classification accuracy at a very low signal-to-noise ratio. Furthermore, the proposed model outperforms the related model in literature in terms of accuracy for the FFT and SCF pre-processed signals.
SponsorQatar National Research Fund
Languageen
PublisherIEEE Computer Society
SubjectClassification (of information)
Convolution
Convolutional neural networks
Deep learning
Fast Fourier transforms
Internet of things
Signal processing
Signal to noise ratio
Convolutional neural network
Cyclostationary signal
Cyclostationary signal processing
Learning models
Lora
LPWAN
Signal classification
Signal-processing
Spectral correlation function
Spreading factor
Cognitive radio
TitleA Deep Learning Model for LoRa Signals Classification Using Cyclostationay Features
TypeConference Paper
Pagination76-81
Volume Number2021-October
dc.accessType Abstract Only


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