rus
DOI: 10.22184/2070-8963.2024.124.8.40.47
The paper is devoted to the review of models of artificial intelligence use for determining by the receiver of cognitive radio information about the target signal structure on the basis of neural network approach. It describes how the models capture and partition LTE and 5G NR signals in spectrum sensing. Deep learning neural network semantic segmentation neural network models are used to identify LTE and NR signals. The considered set of models can be used for practical implementation of spectrum sensing in dynamic spectrum access in promising cognitive radio networks.
Tags: 5g networks artificial intelligence cognitive radio networks spectrum sensing models искусственный интеллект модели спектрального зондирования сети 5g сети когнитивного радио
Subscribe to the journal Last Mile to read the full article.
The paper is devoted to the review of models of artificial intelligence use for determining by the receiver of cognitive radio information about the target signal structure on the basis of neural network approach. It describes how the models capture and partition LTE and 5G NR signals in spectrum sensing. Deep learning neural network semantic segmentation neural network models are used to identify LTE and NR signals. The considered set of models can be used for practical implementation of spectrum sensing in dynamic spectrum access in promising cognitive radio networks.
Tags: 5g networks artificial intelligence cognitive radio networks spectrum sensing models искусственный интеллект модели спектрального зондирования сети 5g сети когнитивного радио
Subscribe to the journal Last Mile to read the full article.
Readers feedback
