Deep learning based real-time spectrum analysis for wireless networks
J Wicht, U Wetzker, A Frotzscher - European Wireless 2021; …, 2021 - ieeexplore.ieee.org
Wireless networks are indispensable in today's production and automation. In an industrial
environment, they are used in particular for networking moving or inaccessible parts of a …
environment, they are used in particular for networking moving or inaccessible parts of a …
Wireless Network Analytics for the New Era of Spectrum Patrolling and Monitoring
E Testi, A Giorgetti - IEEE Wireless Communications, 2024 - ieeexplore.ieee.org
The importance of networks in modern-day society is experiencing rapid and extensive
growth, thanks to remarkable technological advancements. Next-generation wireless …
growth, thanks to remarkable technological advancements. Next-generation wireless …
Big data goes small: Real-time spectrum-driven embedded wireless networking through deep learning in the RF loop
F Restuccia, T Melodia - IEEE INFOCOM 2019-IEEE …, 2019 - ieeexplore.ieee.org
The explosion of 5G networks and the Internet of Things will result in an exceptionally
crowded RF environment, where techniques such as spectrum sharing and dynamic …
crowded RF environment, where techniques such as spectrum sharing and dynamic …
Towards commoditized real-time spectrum monitoring
We are facing an increasingly difficult challenge in spectrum management: how to perform
real-time spectrum monitoring with strong coverage of deployed regions. Today's spectrum …
real-time spectrum monitoring with strong coverage of deployed regions. Today's spectrum …
Spectrum Monitoring Based on End-to-End Learning by Deep Learning
M Rahmani, R Ghazizadeh - International Journal of Wireless Information …, 2022 - Springer
Numerous autonomous wireless deployments have become invaluable for understanding
and investigating the radio frequency environment. However, machine learning techniques …
and investigating the radio frequency environment. However, machine learning techniques …
Spectrogram Data Set for Deep-Learning-Based RF Frame Detection
J Wicht, U Wetzker, V Jain - Data, 2022 - mdpi.com
Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults
in wireless networks such as difficult signal propagation conditions and coexisting wireless …
in wireless networks such as difficult signal propagation conditions and coexisting wireless …
Detection of traffic patterns in the radio spectrum for cognitive wireless network management
Dynamic Spectrum Access allows using the spectrum opportunistically by identifying
wireless technologies sharing the same medium. However, detecting a given technology is …
wireless technologies sharing the same medium. However, detecting a given technology is …
Long-term spectrum monitoring with big data analysis and machine learning for cloud-based radio access networks
Spectrum monitoring is important for efficient spectrum sharing and resource management
in cloud-based radio access networks (C-RAN). In this paper we show how data obtained …
in cloud-based radio access networks (C-RAN). In this paper we show how data obtained …
A novel spectrogram based lightweight deep learning for IoT spectrum monitoring
S Benazzouza, M Ridouani, F Salahdine… - Physical Communication, 2024 - Elsevier
The integration of cognitive radio network (CRNet) with internet of things (IoT) holds
tremendous potential for creating more intelligent and advanced technological ecosystems …
tremendous potential for creating more intelligent and advanced technological ecosystems …
End-to-end learning from spectrum data: A deep learning approach for wireless signal identification in spectrum monitoring applications
This paper presents end-to-end learning from spectrum data-an umbrella term for new
sophisticated wireless signal identification approaches in spectrum monitoring applications …
sophisticated wireless signal identification approaches in spectrum monitoring applications …