Interference suppression using deep learning: Current approaches and open challenges

T Oyedare, VK Shah, DJ Jakubisin, JH Reed - IEEE Access, 2022 - ieeexplore.ieee.org
In light of the finite nature of the wireless spectrum and the increasing demand for spectrum
use arising from recent technological breakthroughs in wireless communication, the problem …

Deep learning for experimental hybrid terrestrial and satellite interference management

P Henarejos, MÁ Vázquez… - 2019 IEEE 20th …, 2019 - ieeexplore.ieee.org
Interference Management is a vast topic present in many disciplines. The majority of
wireless standards suffer the drawback of interference intrusion and the network efficiency …

Keep it simple: Cnn model complexity studies for interference classification tasks

T Oyedare, VK Shah, DJ Jakubisin… - IEEE INFOCOM 2023 …, 2023 - ieeexplore.ieee.org
The growing number of devices using the wireless spectrum makes it important to find ways
to minimize interference and optimize the use of the spectrum. Deep learning models, such …

Learning to optimize: Training deep neural networks for interference management

H Sun, X Chen, Q Shi, M Hong, X Fu… - IEEE Transactions …, 2018 - ieeexplore.ieee.org
Numerical optimization has played a central role in addressing key signal processing (SP)
problems. Highly effective methods have been developed for a large variety of SP …

Interference mitigation in wideband radios using spectrum correlation and neural network

A Toma, T Nawaz, Y Gao, L Marcenaro… - IET …, 2019 - Wiley Online Library
Technologies such as cognitive radio and dynamic spectrum access rely on spectrum
sensing which provides wireless devices with information about the radio spectrum in the …

Spectrum sensing in interference and noise using deep learning

D Chew, AB Cooper - 2020 54th Annual conference on …, 2020 - ieeexplore.ieee.org
Wireless devices are ubiquitous and consequently the spectrum is congested. Dynamic
spectrum access is becoming more widespread in unlicensed bands and as a means to …

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 …

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 …

Interference management in 5G and beyond network: Requirements, challenges and future directions

MUA Siddiqui, F Qamar, F Ahmed, QN Nguyen… - IEEE …, 2021 - ieeexplore.ieee.org
In the modern technological world, wireless communication has taken a massive leap from
the conventional communication system to a new radio communication network. The novel …

Multi-domain networks for wireless interference recognition

P Wang, Y Cheng, B Dong, Q Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In the field of military communications, electromagnetic interference has posed a serious
threat to wireless communication systems, and wireless interference recognition (WIR) is …