Communication spectrum prediction method based on convolutional gated recurrent unit network
Abstract In modern wireless communication systems, the scarcity of spectrum resources poses challenges to the performance and efficiency of the system. Spectrum prediction technology can help systems better plan and schedule resources to respond to the dynamic changes in spectrum. Dynamic change in...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-56311-y |
| _version_ | 1827277251036577792 |
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| author | Lige Yuan Lulu Nie Yangzhou Hao |
| author_facet | Lige Yuan Lulu Nie Yangzhou Hao |
| author_sort | Lige Yuan |
| collection | DOAJ |
| description | Abstract In modern wireless communication systems, the scarcity of spectrum resources poses challenges to the performance and efficiency of the system. Spectrum prediction technology can help systems better plan and schedule resources to respond to the dynamic changes in spectrum. Dynamic change in the spectrum refers to the changes in the radio spectrum in a wireless communication system. It means that the available spectrum resources may change at different times and locations. In response to this current situation, this study first constructs a communication collaborative spectrum sensing model using channel aliasing dense connection networks. Then, combining convolutional neural network and gated cyclic unit network in deep learning technology, a communication spectrum prediction model is built. It aims to achieve accurate perception and prediction of spectrum resources through the aforementioned spectrum sensing and prediction models. The results confirm that the proposed perception model has inconsistent perception accuracy under different number of secondary users, with a maximum of 0.99. It is verified that the proposed spectrum prediction model achieves a high prediction accuracy of 0.95 within 208 s and its performance outperforms current similar models. The results are based on the model's deep learning analysis of massive historical communication data, in which the optimized shuffle dense net model plus convolutional gated recurrent unit model is the key to achieve fast and accurate prediction. On the contrary, the highest spectrum prediction accuracy of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks-Long Short-Term Memory (ConvLSTM) models are 0.86, 0.90, and 0.85, respectively. And the model needs to run for a longer period of time, up to 324, for ConvLSTM to reach the prediction accuracy value of 0.95. In summary, the perception and prediction model built by this research has good performance, and its application in the field of wireless communication can assist staff in better monitoring spectral changes, thereby making more efficient use of spectral resources. |
| first_indexed | 2024-04-24T07:17:48Z |
| format | Article |
| id | doaj.art-cb8917b844e64c20ad0eeec803113a9a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T07:17:48Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-cb8917b844e64c20ad0eeec803113a9a2024-04-21T11:14:25ZengNature PortfolioScientific Reports2045-23222024-04-0114111710.1038/s41598-024-56311-yCommunication spectrum prediction method based on convolutional gated recurrent unit networkLige Yuan0Lulu Nie1Yangzhou Hao2Information Engineering College, Zhengzhou Technology and Business UniversityInformation Engineering College, Zhengzhou Technology and Business UniversityUnderground Pipeline Detection Institute, Mineral Resources Exploration Center of Henan Geological BureauAbstract In modern wireless communication systems, the scarcity of spectrum resources poses challenges to the performance and efficiency of the system. Spectrum prediction technology can help systems better plan and schedule resources to respond to the dynamic changes in spectrum. Dynamic change in the spectrum refers to the changes in the radio spectrum in a wireless communication system. It means that the available spectrum resources may change at different times and locations. In response to this current situation, this study first constructs a communication collaborative spectrum sensing model using channel aliasing dense connection networks. Then, combining convolutional neural network and gated cyclic unit network in deep learning technology, a communication spectrum prediction model is built. It aims to achieve accurate perception and prediction of spectrum resources through the aforementioned spectrum sensing and prediction models. The results confirm that the proposed perception model has inconsistent perception accuracy under different number of secondary users, with a maximum of 0.99. It is verified that the proposed spectrum prediction model achieves a high prediction accuracy of 0.95 within 208 s and its performance outperforms current similar models. The results are based on the model's deep learning analysis of massive historical communication data, in which the optimized shuffle dense net model plus convolutional gated recurrent unit model is the key to achieve fast and accurate prediction. On the contrary, the highest spectrum prediction accuracy of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks-Long Short-Term Memory (ConvLSTM) models are 0.86, 0.90, and 0.85, respectively. And the model needs to run for a longer period of time, up to 324, for ConvLSTM to reach the prediction accuracy value of 0.95. In summary, the perception and prediction model built by this research has good performance, and its application in the field of wireless communication can assist staff in better monitoring spectral changes, thereby making more efficient use of spectral resources.https://doi.org/10.1038/s41598-024-56311-yRecurrent neural networkConvolutionalDoor control unitCommunication spectrumPredictionRadio |
| spellingShingle | Lige Yuan Lulu Nie Yangzhou Hao Communication spectrum prediction method based on convolutional gated recurrent unit network Scientific Reports Recurrent neural network Convolutional Door control unit Communication spectrum Prediction Radio |
| title | Communication spectrum prediction method based on convolutional gated recurrent unit network |
| title_full | Communication spectrum prediction method based on convolutional gated recurrent unit network |
| title_fullStr | Communication spectrum prediction method based on convolutional gated recurrent unit network |
| title_full_unstemmed | Communication spectrum prediction method based on convolutional gated recurrent unit network |
| title_short | Communication spectrum prediction method based on convolutional gated recurrent unit network |
| title_sort | communication spectrum prediction method based on convolutional gated recurrent unit network |
| topic | Recurrent neural network Convolutional Door control unit Communication spectrum Prediction Radio |
| url | https://doi.org/10.1038/s41598-024-56311-y |
| work_keys_str_mv | AT ligeyuan communicationspectrumpredictionmethodbasedonconvolutionalgatedrecurrentunitnetwork AT lulunie communicationspectrumpredictionmethodbasedonconvolutionalgatedrecurrentunitnetwork AT yangzhouhao communicationspectrumpredictionmethodbasedonconvolutionalgatedrecurrentunitnetwork |