This paper proposes a deep learning method based on a lightweight hybrid attention network. In order to capture the key features of 5G data more effectively,
The efficient hybrid attention module makes the processing of 5G network traffic data more targeted, with the model giving higher weights to key data as a way ...
Jan 5, 2024 · Existing deep learning methods have been able to predict network traffic to a certain extent, so as to solve the communication efficiency and ...
The maturity of 5G technology provides a guarantee for increasingly large communication networks, while the resources required for communication and ...
Traffic prediction for 5G: A deep learning approach based on lightweight hybrid attention networks. J. Su, H. Cai, Z. Sheng, A. Liu, and A. Baz.
Jun 14, 2022 · This paper makes an accurate prediction of the 5G network and builds a smoothed long short-term memory (SLSTM) traffic prediction model.
Apr 1, 2024 · The deep learning module uses the output of the traffic classifier to predict the real-time trends of 5G mobile traffic. The lightweight module ...
To solve this issue, we introduced CNN+LSTM, a hybrid model that combines CNN, and LSTM to forecast cumulative network traffic across particular intervals to ...
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Jun 24, 2022 · This paper makes an accurate prediction of the 5G network and builds a smoothed long short-term memory (SLSTM) traffic prediction model.
Missing: lightweight attention
This survey focuses on a data-driven method for cellular traffic prediction, which is essentially time-series forecasting. Traditional time-series forecasting ...