Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks
This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a c...
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MDPI AG
2021-04-01
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Online Access: | https://www.mdpi.com/1424-8220/21/7/2414 |
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author | Ebtesam Almazrouei Gabriele Gianini Nawaf Almoosa Ernesto Damiani |
author_facet | Ebtesam Almazrouei Gabriele Gianini Nawaf Almoosa Ernesto Damiani |
author_sort | Ebtesam Almazrouei |
collection | DOAJ |
description | This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate—using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards—that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions. |
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institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-03-10T12:43:28Z |
publishDate | 2021-04-01 |
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series | Sensors |
spelling | doaj.art-5c53cdf3682c40f3b920d15319e091302023-11-21T13:40:28ZengMDPI AGSensors1424-82202021-04-01217241410.3390/s21072414Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural NetworksEbtesam Almazrouei0Gabriele Gianini1Nawaf Almoosa2Ernesto Damiani3Emirates ICT Innovation Centre, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab EmiratesEmirates ICT Innovation Centre, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab EmiratesEmirates ICT Innovation Centre, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab EmiratesEmirates ICT Innovation Centre, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab EmiratesThis paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate—using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards—that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions.https://www.mdpi.com/1424-8220/21/7/2414Deep LearningDenoising Autoencodersconvolutional neural networksclassificationIEEE Wi-Fi protocolsLTE |
spellingShingle | Ebtesam Almazrouei Gabriele Gianini Nawaf Almoosa Ernesto Damiani Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks Sensors Deep Learning Denoising Autoencoders convolutional neural networks classification IEEE Wi-Fi protocols LTE |
title | Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks |
title_full | Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks |
title_fullStr | Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks |
title_full_unstemmed | Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks |
title_short | Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks |
title_sort | robust computationally efficient wireless emitter classification using autoencoders and convolutional neural networks |
topic | Deep Learning Denoising Autoencoders convolutional neural networks classification IEEE Wi-Fi protocols LTE |
url | https://www.mdpi.com/1424-8220/21/7/2414 |
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