Network traffic grant classification based on 1DCNN-TCN-GRU hybrid model
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10489-024-05375-4.pdf
Reference51 articles.
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3. D’Angelo G, Palmieri F (2021) Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial-temporal features extraction. J Netw Comput Appl 173:1–16. https://doi.org/10.1016/j.jnca.2020.102890
4. Yang H, Li X, Qiang W, Zhao Y, Zhang W, Tang C (2021) A network traffic forecasting method based on sa optimized arima-bp neural network. Comput Netw 193:1–12. https://doi.org/10.1016/j.comnet.2021.108102
5. Brand P, Sabih M, Falk J, Sue JA, Teich J (2020) Clustering-based scenario-aware lte grant prediction. In: 2020 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp 1–7. https://doi.org/10.1109/WCNC45663.2020.9120789
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