Toward Intelligence in Communication Networks: A Deep Learning Identification Strategy for Radio Frequency Fingerprints

2021 ◽  
Author(s):  
Kangjun Bai ◽  
Clare Thiem ◽  
Nathan McDonald ◽  
Lisa Loomis ◽  
Yang Yi
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Identification Strategy

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Jithin Jagannath ◽  
Anu Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Jithin Jagannath ◽  
Anu Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

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 Performance Analysis of Hybrid Radio Frequency and Free Space Optical Communication Networks with Cooperative Spectrum Sharing

Photonics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 108
Author(s):  
Dong Qin ◽  
Yuhao Wang ◽  
Tianqing Zhou
Keyword(s):  
Radio Frequency ◽  
Communication Networks ◽  
Free Space ◽  
Spectrum Sharing ◽  
Cumulative Distribution ◽  
Free Space Optical ◽  
Secondary Users ◽  
Primary Users ◽  
Cooperative Spectrum Sharing ◽  
The Impact

This paper investigates the impact of cooperative spectrum sharing policy on the performance of hybrid radio frequency and free space optical wireless communication networks, where primary users and secondary users develop a band of the same spectrum resource. The radio frequency links obey Nakagami-m distribution with arbitrary fading parameter m, while the free space optical link follows gamma-gamma distributed atmospheric turbulence with nonzero pointing error. Because the secondary users access the spectrum band without payment, their behavior needs to be restricted. Specifically, the power of the secondary users is dominated by the tolerable threshold of the primary users. Considering both heterodyne and intensity modulation/direct detection strategies in optical receiver, the performance of optical relaying networks is completely different from that of traditional networks. With the help of bivariable Fox’s H function, new expressions for cumulative distribution function of equivalent signal to noise ratio at destination, probability density function, outage probability, ergodic capacity and symbol error probability are built in closed forms.

scholarly journals Hygienic assessment of the impact of 5G/IMT-2020 communication networks on public health (literature review)

2021 ◽  
Vol 100 (9) ◽  
pp. 929-932
Author(s):  
Anna M. Egorova ◽  
Lydiya A. Lutsenko ◽  
Anna V. Sukhova ◽  
Vyacheslav V. Kolyuka ◽  
Rustam V. Turdyev
Keyword(s):  
Public Health ◽  
Radio Frequency ◽  
Electromagnetic Radiation ◽  
Communication Networks ◽  
Biological Effects ◽  
Three Dimensional ◽  
Cochrane Library ◽  
Pubmed Search ◽  
The Cochrane Library ◽  
The Impact

The program “Digital Economy of the Russian Federation” approved the Concept for the creation and development of 5G / IMT-2020 networks. The development of 5G communications will significantly impact the implementation of many innovative projects and initiatives: the Smart City project, Unmanned Transport, etc. Along with significant technical advantages compared to previous generations of communication (2G, 3G, 4G), 5G technology has completely different emitting characteristics: more emitting elements, signal modulation, three-dimensional beam, the ability to control the beam, SHF (ultra-high) and EHF (extremely high) radio frequency ranges and centimetre and millimetre wavelengths of electromagnetic radiation. Therefore, it is becoming an especially urgent problem to ensure exposure to the human body of non-ionizing electromagnetic fields of the radio frequency range (30 kHz-300 GHz). The authors searched the literature on the biological effects of 5G cellular communications and electromagnetic radiation in the centimetre and millimetre ranges using the appropriate keywords in PubMed search engines, Scopus, Web of Science, Medline, The Cochrane Library, EMBASE, Global Health, CyberLeninka, RSCI and others. There is currently tentative and conflicting evidence on the impact of 5G. The rapidly growing density of wireless devices and antennas (considering future 5G networks) increases the public health risk from exposure to RF EMFs as the penetration depth for 5G EHF radiation is only a few millimetres. At these wavelengths, resonance phenomena are possible at the cellular and molecular levels, particularly concerning stimulating SHF and EHF oxidative processes and damaging DNA. The influence of the millimetre range of RF-EMF is poorly understood; oncological and non-oncological (impact on the reproductive, immune systems, etc.) effects are possible. Using numerical simulation methods of EMF radiation resonances on insects, Thielens A et al., 2018, found a significant overall increase in the absorbed RF power at a frequency of 6 GHz and higher than a frequency below 6 GHz.

scholarly journals Deep learning improves identification of Radio Frequency Interference

2020 ◽  
Vol 499 (1) ◽  
pp. 379-390
Author(s):  
Alireza Vafaei Sadr ◽  
Bruce A Bassett ◽  
Nadeem Oozeer ◽  
Yabebal Fantaye ◽  
Chris Finlay
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Radio Astronomy ◽  
Real Data ◽  
Radio Frequency Interference ◽  
Time Frequency ◽  
Amplitude Data ◽  
Important Challenge ◽  
Telescope Arrays

ABSTRACT Flagging of Radio Frequency Interference (RFI) in time–frequency visibility data is an increasingly important challenge in radio astronomy. We present R-Net, a deep convolutional ResNet architecture that significantly outperforms existing algorithms – including the default MeerKAT RFI flagger, and deep U-Net architectures – across all metrics including AUC, F1-score, and MCC. We demonstrate the robustness of this improvement on both single dish and interferometric simulations and, using transfer learning, on real data. Our R-Net model’s precision is approximately $90{{\ \rm per\ cent}}$ better than the current MeerKAT flagger at $80{{\ \rm per\ cent}}$ recall and has a 35 per cent higher F1-score with no additional performance cost. We further highlight the effectiveness of transfer learning from a model initially trained on simulated MeerKAT data and fine-tuned on real, human-flagged, KAT-7 data. Despite the wide differences in the nature of the two telescope arrays, the model achieves an AUC of 0.91, while the best model without transfer learning only reaches an AUC of 0.67. We consider the use of phase information in our models but find that without calibration the phase adds almost no extra information relative to amplitude data only. Our results strongly suggest that deep learning on simulations, boosted by transfer learning on real data, will likely play a key role in the future of RFI flagging of radio astronomy data.

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