Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization

Ujan, Sahar; Navidi, Néda; Landry, René

doi:10.20944/preprints202005.0356.v1

Cited by 1 publication

(3 citation statements)

References 18 publications

(31 reference statements)

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“…This study proposes a DL-based approach for RFI recognition and AMC by benefiting from transfer learning strategy. The general framework is based on the hierarchical classification proposed in [6], which the first and second levels determine the type of the received signal that is either SoI or a combination of SoI with any of the jamming signals and the modulation type of SoI, respectively. To this end, in the first classification level, a classifier is trained to determine the type of the received signals.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…As fully explained in [5,6], the desired signal is a real-time video stream, which is modulated and processed by GNU radio and transmitted using a Universal Software Radio Peripheral (USRP-N210) [22]. For modeling a real-time Satcom channel simulator (RTLogic T400) [23] is used.…”

Section: Data Acquisition Set-upmentioning

confidence: 99%

“…Since, Automatic Modulation Classification (AMC) is a significant procedure in communication networks to facilitate the demodulation process at the receiver side [4]. To address this concern, Machine Learning (ML) based techniques have shown promising results in the area of multi-class RFI recognition [5,6] and Automatic modulation classification (AMC) [6]. However, the complex nature of tasks like pre-processing, feature extraction, feature selection etc in classical ML techniques highly degrades the classification precision regarding efficiency and accuracy [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Radio Frequency Interference Recognition Using End-to-end Transfer Learning

Ujan¹,

Navidi²,

Landry³

2020

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Radio Frequency Interference (RFI) detection and characterization play a critical role to 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, this paper proposes an efficient end-to-end method using the latest advances in deep learning to extract the appropriate features of the RFI signal. Moreover, this study utilizes the benefits of transfer learning to determine both the type of received RFI signals and their modulation types. To this end, the scalogram of the received signals is used as the input of the pre-trained 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 pre-trained 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).

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Data Acquisition Set-upmentioning

confidence: 99%