5G Traffic Prediction Based on Deep Learning. (24th June 2022)
- Record Type:
- Journal Article
- Title:
- 5G Traffic Prediction Based on Deep Learning. (24th June 2022)
- Main Title:
- 5G Traffic Prediction Based on Deep Learning
- Authors:
- Gao, Zihang
- Other Names:
- Gu Yang Academic Editor.
- Abstract:
- Abstract : The demand of wireless access users is increasing explosively. The 5G network traffic is increasing exponentially and showing a trend of diversity and heterogeneity, which makes network traffic forecasting face many challenges. By studying the actual performance of the 5G network, this paper makes an accurate prediction of the 5G network and builds a smoothed long short-term memory (SLSTM) traffic prediction model. The model updates the number of layers and hidden units according to the prediction accuracy adaptive mechanism. At the same time, in order to reduce the randomness of the 5G traffic sequence, the output feature sequence of the original time series is stabilized by the seasonal time difference method. In the experiments, the prediction results of the proposed algorithm are compared with those of the traditional algorithms. The results show that the SLSTM algorithm can effectively improve the accuracy of 5G traffic prediction. The model can be used for 5G traffic prediction for decision-making.
- Is Part Of:
- Computational intelligence and neuroscience. Volume 2022(2022)
- Journal:
- Computational intelligence and neuroscience
- Issue:
- Volume 2022(2022)
- Issue Display:
- Volume 2022, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 2022
- Issue:
- 2022
- Issue Sort Value:
- 2022-2022-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-06-24
- Subjects:
- Neurosciences -- Data processing -- Periodicals
Computational intelligence -- Periodicals
Computational neuroscience -- Periodicals
612.80285 - Journal URLs:
- https://www.hindawi.com/journals/cin/ ↗
- DOI:
- 10.1155/2022/3174530 ↗
- Languages:
- English
- ISSNs:
- 1687-5265
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 22295.xml