An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning
Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/19/6885 |
| _version_ | 1797552080958783488 |
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| author | Sahar Ujan Neda Navidi Rene Jr Landry |
| author_facet | Sahar Ujan Neda Navidi Rene Jr Landry |
| author_sort | Sahar Ujan |
| collection | DOAJ |
| description | Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR). |
| first_indexed | 2024-03-10T15:54:53Z |
| format | Article |
| id | doaj.art-54c3427ffd1c4c00907d71fc10f842f6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:54:53Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-54c3427ffd1c4c00907d71fc10f842f62023-11-20T15:45:07ZengMDPI AGApplied Sciences2076-34172020-10-011019688510.3390/app10196885An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer LearningSahar Ujan0Neda Navidi1Rene Jr Landry2LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, CanadaLASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, CanadaLASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, CanadaRadio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).https://www.mdpi.com/2076-3417/10/19/6885radio frequency interference detectiondeep learningtransfer learningpretrained convolutional neural networks |
| spellingShingle | Sahar Ujan Neda Navidi Rene Jr Landry An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning Applied Sciences radio frequency interference detection deep learning transfer learning pretrained convolutional neural networks |
| title | An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning |
| title_full | An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning |
| title_fullStr | An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning |
| title_full_unstemmed | An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning |
| title_short | An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning |
| title_sort | efficient radio frequency interference rfi recognition and characterization using end to end transfer learning |
| topic | radio frequency interference detection deep learning transfer learning pretrained convolutional neural networks |
| url | https://www.mdpi.com/2076-3417/10/19/6885 |
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