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Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom

LASSENA Laboratory, Ecole de Technologie Superieure (ETS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada
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Appl. Sci. 2020, 10(13), 4608; https://doi.org/10.3390/app10134608
Received: 21 May 2020 / Revised: 18 June 2020 / Accepted: 25 June 2020 / Published: 3 July 2020
(This article belongs to the Section Computing and Artificial Intelligence)
The Quality of Service (QoS) and security of Satellite Communication (Satcom) are crucial as Satcom plays a significant role in a wide range of applications, such as direct broadcast satellite, earth observation, navigation, and government/military systems. Therefore, it is necessary to ensure that transmissions are incorruptible, particularly in the presence of challenges such as Radio Frequency Interference (RFI), which is of primary concern for the efficiency of communications. The security of a wireless communication system can be improved using a robust RFI detection method, which could, in turn, lead to an effective mitigation process. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron (MLP) neural network. The considered characteristics are signal modulation and the type of RFI. In the experiments, a real-time video stream transmitted in the direct broadcast satellite is utilized with four modulation types, namely, QPSK, 8APSK, 16APSK, and 32APSK. Moreover, it is assumed that the communication signal can be combined with one of the three significant types of interference, namely, Continuous Wave Interference (CWI), Multiple CWI (MCWI), and Chirp Interference (CI). In addition, two robust feature selection techniques have been developed to select more informative features, which leads to improving the classification precision. Furthermore, the robustness of the trained techniques is assessed to predict unknown signals at different Signal to Noise Ratios (SNRs). View Full-Text
Keywords: supervised learning; time series classification; jamming detection; Automatic Modulation Classification; feature selection; Genetic Algorithm; Principal Component Analysis; QPSK modulation; APSK modulation supervised learning; time series classification; jamming detection; Automatic Modulation Classification; feature selection; Genetic Algorithm; Principal Component Analysis; QPSK modulation; APSK modulation
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MDPI and ACS Style

Ujan, S.; Navidi, N.; Landry, R.J. Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom. Appl. Sci. 2020, 10, 4608. https://doi.org/10.3390/app10134608

AMA Style

Ujan S, Navidi N, Landry RJ. Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom. Applied Sciences. 2020; 10(13):4608. https://doi.org/10.3390/app10134608

Chicago/Turabian Style

Ujan, Sahar; Navidi, Neda; Landry, Rene J. 2020. "Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom" Appl. Sci. 10, no. 13: 4608. https://doi.org/10.3390/app10134608

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