Deep neural networks for spectrum sensing: a review
As we advance towards 6G communication systems, the number of network devices
continues to increase resulting in spectrum scarcity. With the help of Spectrum Sensing (SS) …
continues to increase resulting in spectrum scarcity. With the help of Spectrum Sensing (SS) …
[图书][B] Cognitive Electronic Warfare: An Artificial Intelligence Approach
K Haigh, J Andrusenko - 2021 - books.google.com
This comprehensive book gives an overview of how cognitive systems and artificial
intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems …
intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems …
Deep learning for radar signal detection in the 3.5 GHz CBRS band
R Caromi, A Lackpour, K Kallas… - 2021 IEEE …, 2021 - ieeexplore.ieee.org
This paper presents a comprehensive framework for generating radio frequency (RF)
datasets, designing deep learning (DL) detectors, and evaluating their detection …
datasets, designing deep learning (DL) detectors, and evaluating their detection …
A spatial-diversity MIMO dataset for RF signal processing research
P Ghasemzadeh, M Hempel… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The procedure of classifying a detected signal's modulation scheme with no a priori
information is known as automatic modulation classification (AMC). AMC has presented …
information is known as automatic modulation classification (AMC). AMC has presented …
Deep learning approaches for spectrum sensing in cognitive radio networks
The number of network devices continues to rise as we advance towards 6G communication
systems. A new range of frequencies is allocated while the earlier resources remain …
systems. A new range of frequencies is allocated while the earlier resources remain …
Improving Radio Network Planning and Design in Next-Generation Mobile Networks Using AI and ML Algorithms
This research study discusses the use of machine learning (ML) algorithms in improving the
radio network planning and design in next-generation mobile networks (NGMN). The article …
radio network planning and design in next-generation mobile networks (NGMN). The article …
Data collection and generation for radio frequency signal security
TA Youssef, GA Francia, III, HE Sevil - … Proceedings from SAM'20, ICWN'20 …, 2021 - Springer
The current proliferation of unmanned aerial systems (UASs) for a wide range of
applications ranging from commercial to defence purposes demands the need for their …
applications ranging from commercial to defence purposes demands the need for their …
Bluetooth Low Energy (BLE) RF Dataset for Machine Learning in WBANs
SM Kashani, S Sherazi, A Khokhar… - 2024 IEEE Wireless …, 2024 - ieeexplore.ieee.org
The lack of availability of real-world RF datasets has often impeded physical layer research
relating to IoT and Health IoT sectors. Towards this end, this paper presents an …
relating to IoT and Health IoT sectors. Towards this end, this paper presents an …
A novel graph neural network-based framework for automatic modulation classification in mobile environments
P Ghasemzadeh - 2023 - search.proquest.com
Automatic modulation classification (AMC) refers to a signal processing procedure through
which the modulation type and order of an observed signal are identified without any prior …
which the modulation type and order of an observed signal are identified without any prior …
[HTML][HTML] RF dataset of incumbent radar signals in the 3.5 GHz CBRS band
R Caromi, M Souryal, TA Hall - Journal of Research of the National …, 2019 - ncbi.nlm.nih.gov
1. Summary This Radio Frequency (RF) dataset consists of synthetically generated
waveforms of incumbent3. 5 GHz radar systems. The intended use of the dataset is for …
waveforms of incumbent3. 5 GHz radar systems. The intended use of the dataset is for …