Deep learning frameworks for cognitive radio networks: Review and open research challenges
Deep learning has been proven to be a powerful tool for addressing the most significant
issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource …
issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource …
A Review of Research on Spectrum Sensing Based on Deep Learning
Y Zhang, Z Luo - Electronics, 2023 - mdpi.com
In recent years, with the rapid development in wireless communication and 5G networks, the
rapid growth in mobile users has been accompanied by an increasing demand for the …
rapid growth in mobile users has been accompanied by an increasing demand for the …
Spectrum sharing schemes from 4G to 5G and beyond: Protocol flow, regulation, ecosystem, economic
As the services and requirements of next-generation wireless networks become increasingly
diversified, it is estimated that the current frequency bands of mobile network operators …
diversified, it is estimated that the current frequency bands of mobile network operators …
Federated learning-based cooperative spectrum sensing in cognitive radio
Z Chen, YQ Xu, H Wang, D Guo - IEEE Communications …, 2021 - ieeexplore.ieee.org
Deep cooperative sensing is a cooperative spectrum sensing (CSS) algorithm based on a
deep neural network (DNN). Since training DNN requires a large amount of sample data …
deep neural network (DNN). Since training DNN requires a large amount of sample data …
Collaborative Learning Based Spectrum Sensing Under Partial Observations
To deal with the complex wireless cognitive radios, data-driven learning technologies have
been advocated for spectrum sensing. While the existing learning-based methods are …
been advocated for spectrum sensing. While the existing learning-based methods are …
A comprehensive survey of spectrum sharing schemes from a standardization and implementation perspective
As the services and requirements of next-generation wireless networks become increasingly
diversified, it is estimated that the current frequency bands of mobile network operators …
diversified, it is estimated that the current frequency bands of mobile network operators …
A review of deep learning techniques for enhancing spectrum sensing and prediction in cognitive radio systems: approaches, datasets, and challenges
Cognitive radio (CR) is an emerging wireless technology designed to optimize frequency
band usage and address spectrum shortages. Spectrum sensing and prediction are crucial …
band usage and address spectrum shortages. Spectrum sensing and prediction are crucial …
Deep cooperative spectrum sensing based on residual neural network using feature extraction and random forest classifier
Some bands in the frequency spectrum have become overloaded and others underutilized
due to the considerable increase in demand and user allocation policy. Cognitive radio …
due to the considerable increase in demand and user allocation policy. Cognitive radio …
Spectrum Transformer: An Attention-based Wideband Spectrum Detector
Data-driven machine learning techniques have been advocated for signal detection in
complex wireless environments. However, when applied to wideband spectrum sensing …
complex wireless environments. However, when applied to wideband spectrum sensing …
Cooperative spectrum sensing system using residual convolutional neural network
Some bands in the frequency spectrum became overloaded and others underutilized due to
considerable increase in demand and user allocation policy. Cognitive radio applies …
considerable increase in demand and user allocation policy. Cognitive radio applies …