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المؤلفAlmohamad A.
المؤلفHasna , Mazen
المؤلفAlthunibat S.
المؤلفTekbiyik K.
المؤلفQaraqe K.
تاريخ الإتاحة2022-04-26T11:06:45Z
تاريخ النشر2021
اسم المنشورInternational Conference on ICT Convergence
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/ICTC52510.2021.9621015
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85122945783&doi=10.1109%2fICTC52510.2021.9621015&partnerID=40&md5=fdf655733fafa83e8dba26ad2cb35699
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30442
الملخصWith 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.
راعي المشروعQatar National Research Fund
اللغةen
الناشرIEEE Computer Society
الموضوعClassification (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
العنوانA Deep Learning Model for LoRa Signals Classification Using Cyclostationay Features
النوعConference Paper
الصفحات76-81
رقم المجلد2021-October
dc.accessType Abstract Only


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