Radio frequency interference detection using deep learning

Y Ghanney, W Ajib - 2020 IEEE 91st vehicular technology …, 2020 - ieeexplore.ieee.org
Radio frequency interference (RFI) is considered as anomalous disruptive parasite signal
due to its harmful impact in wireless communication. That is why, RFI mitigation is …

Joint detection and classification of RF signals using deep learning

A Vagollari, V Schram, W Wicke… - 2021 IEEE 93rd …, 2021 - ieeexplore.ieee.org
With the rapid expansion of wireless technologies, monitoring and regulating the Radio
Frequency (RF) spectrum usage becomes more important than ever. In this paper, we …

Interference source identification for ieee 802.15. 4 wireless sensor networks using deep learning

S Yi, H Wang, W Xue, X Fan, L Wang… - 2018 IEEE 29th …, 2018 - ieeexplore.ieee.org
Due to the interference issue in unlicensed band, sensor nodes frequently encounter
degraded performance or lack of connection. This paper provides a real-time external …

Deep learning for interference identification: Band, training SNR, and sample selection

X Zhang, T Seyfi, S Ju, S Ramjee… - 2019 IEEE 20th …, 2019 - ieeexplore.ieee.org
We study the problem of interference source identification, through the lens of recognizing
one of 15 different channels that belong to 3 different wireless technologies: Bluetooth …

Time-frequency component-aware convolutional neural network for wireless interference classification

P Wang, Y Cheng, G Shang, J Wang… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
In wireless communication systems, wireless interference classification (WIC) is considered
as one of the most effective technologies to address the challenges brought by …

An efficient radio frequency interference (RFI) recognition and characterization using end-to-end transfer learning

S Ujan, N Navidi, R Jr Landry - Applied Sciences, 2020 - mdpi.com
Radio Frequency Interference (RFI) detection and characterization play a critical role in
ensuring the security of all wireless communication networks. Advances in Machine …

Drone detection approach based on radio-frequency using convolutional neural network

S Al-Emadi, F Al-Senaid - 2020 IEEE International Conference …, 2020 - ieeexplore.ieee.org
Recently, Unmanned Aerial Vehicles, also known as drones, are becoming rapidly popular
due to the advancement of their technology and the significant decrease in their cost …

Interference detection and recognition based on signal reconstruction using recurrent neural network

Q Wu, Z Sun, X Zhou - 2019 IEEE Globecom Workshops (GC …, 2019 - ieeexplore.ieee.org
Interference detection using deep neural network has recently received increasing attention
due to its capability in learning rich features of data. In this paper, we proposed a low …

Multi-depth adaptive networks for wireless interference identification

P Wang, Y Cheng, B Dong - ICC 2021-IEEE International …, 2021 - ieeexplore.ieee.org
Wireless interference identification (WII) is a promising technology for non-cooperative
communication systems in both civilian and military scenarios. With the rapid development …

RF-based UAV surveillance system: A sequential convolution neural networks approach

R Akter, VS Doan, GB Tunze, JM Lee… - … on Information and …, 2020 - ieeexplore.ieee.org
In recent years, popularity of commercial unmanned air vehicles (UAVs) or drones
enormously increased due to their ductility and availability in various applications domains …