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

2020 ◽  
Vol 10 (13) ◽  
pp. 4608 ◽  
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Radio Frequency ◽  
Satellite Communication ◽  
Continuous Wave ◽  
Hierarchical Classification ◽  
Radio Frequency Interference ◽  
Primary Concern ◽  
Communication Signal ◽  
Wide Range ◽  
Direct Broadcast ◽  
Direct Broadcast Satellite

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).

scholarly journals Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization

2020 ◽  
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Selection ◽  
Radio Frequency ◽  
Satellite Communication ◽  
Hierarchical Classification ◽  
Classification Method ◽  
Radio Frequency Interference ◽  
Data Set ◽  
Communication Signal ◽  
Wide Range

Satellite communication (Satcom) is an artificial geostationary satellite that facilitates a wide range of telecommunications. Considering its quality of service (QoS) and security is crucial in government/military applications. The most challenging situation for efficient Satcom is radio frequency interference (RFI) environment. Thus, it is necessary to ensure that transmissions are incorruptible or at least sense the quality of its spectrum. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron neural network. We consider signal modulation and the type of RFI as the characteristics of a real-time video stream transmitted in the direct broadcast satellite. Four different modulation types are investigated in this study. Moreover, the combination of the communication signal with various kinds of interference and their effects on the classification method widely have been analyzed. Besides, two robust feature selection techniques have been developed to reduce the data-set dimensional, which leads to optimizing the classification process. The results show that the Genetic Algorithm (GA) slightly outperforms Principal Component Analysis (PCA) for feature selection. Furthermore, the robustness of the proposed techniques is assessed to detect unknown signals at different signal to noise ratios.

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

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).

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

2020 ◽  
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
End To End ◽  
Continuous Wave Interference

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).

ANALYSIS OF POTENTIAL RADIO FREQUENCY INTERFERENCE ON ANTENNA POSITIONER (3M) IN RANCABUNGUR GROUND STATION

2019 ◽  
Author(s):  
Rumadi Rumadi ◽  
◽  
Dicka Ariptian Rahayu ◽  
Nur Salma Yusuf Hasanah ◽  
Zhauhar Rainaldy Ardhana ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Interference ◽  
Ground Station

Development of a 1497-MHz, 13.5-kW Continuous-Wave Magnetron for Superconducting Radio Frequency Accelerator

2021 ◽  
pp. 1-9
Author(s):  
Wenliang Li ◽  
Pengjiao Zhang ◽  
Bowen Zhou ◽  
Hong Zhang ◽  
Youchun Liu ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Superconducting Radio Frequency

scholarly journals Multiple Narrowband Interferences Characterization, Detection and Mitigation Using Simplified Welch Algorithm and Notch Filtering

2021 ◽  
Vol 11 (3) ◽  
pp. 1331
Author(s):  
Mohammad Hossein Same ◽  
Gabriel Gleeton ◽  
Gabriel Gandubert ◽  
Preslav Ivanov ◽  
Rene Jr Landry
Keyword(s):  
Power Spectrum ◽  
Embedded System ◽  
Radio Frequency ◽  
Satellite Communication ◽  
Signal To Noise Ratio ◽  
Center Frequency ◽  
Infinite Impulse Response ◽  
Frequency Variation ◽  
Welch Method ◽  
The Embedded System

By increasing the demand for radio frequency (RF) and access of hackers and spoofers to low price hardware and software defined radios (SDR), radio frequency interference (RFI) became a more frequent and serious problem. In order to increase the security of satellite communication (Satcom) and guarantee the quality of service (QoS) of end users, it is crucial to detect the RFI in the desired bandwidth and protect the receiver with a proper mitigation mechanism. Digital narrowband signals are so sensitive into the interference and because of their special power spectrum shape, it is hard to detect and eliminate the RFI from their bandwidth. Thus, a proper detector requires a high precision and smooth estimation of input signal power spectral density (PSD). By utilizing the presented power spectrum by the simplified Welch method, this article proposes a solid and effective algorithm that can find all necessary interference parameters in the frequency domain while targeting practical implantation for the embedded system with minimum complexity. The proposed detector can detect several multi narrowband interferences and estimate their center frequency, bandwidth, power, start, and end of each interference individually. To remove multiple interferences, a chain of several infinite impulse response (IIR) notch filters with multiplexers is proposed. To minimize damage to the original signal, the bandwidth of each notch is adjusted in a way that maximizes the received signal to noise ratio (SNR) by the receiver. Multiple carrier wave interferences (MCWI) is utilized as a jamming attack to the Digital Video Broadcasting-Satellite-Second Generation (DVB-S2) receiver and performance of a new detector and mitigation system is investigated and validated in both simulation and practical tests. Based on the obtained results, the proposed detector can detect a weak power interference down to −25 dB and track a hopping frequency interference with center frequency variation speed up to 3 kHz. Bit error ratio (BER) performance shows 3 dB improvement by utilizing new adaptive mitigation scenario compared to non-adaptive one. Finally, the protected DVB-S2 can receive the data with SNR close to the normal situation while it is under the attack of the MCWI jammer.

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