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Journal Article
Bi‐LSTM model with time distribution for bandwidth prediction in mobile networks
- Authors
- Hyeonji Lee, Yoohwa Kang, Minju Gwak, Donghyeok An
- Issue Date
- 2024-04
- Citation
- ETRI Journal, v.46, no.2, pp.205-217
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- We propose a bandwidth prediction approach based on deep learning. The approach is intended to accurately predict the bandwidth of various types of mobile networks. We first use a machine learning technique, namely, the gradient boosting algorithm, to recognize the connected mobile network. Second, we apply a handover detection algorithm based on network recognition to account for vertical handover that causes the bandwidth variance. Third, as the communication performance offered by 3G, 4G, and 5G networks varies, we suggest a bidirectional long short‐term memory model with time distribution for bandwidth prediction per network. To increase the prediction accuracy, pretraining and fine‐tuning are applied for each type of network. We use a dataset collected at University College Cork for network recognition, handover detection, and bandwidth prediction. The performance evaluation indicates that the handover detection algorithm achieves 88.5% accuracy, and the bandwidth prediction model achieves a high accuracy, with a root‐mean‐square error of only 2.12%.
- KSP Keywords
- 5G Network, Bandwidth prediction, Boosting algorithm, Communication performance, Detection algorithm, High accuracy, Machine Learning technique(MLT), Memory Model, Mobile networks, Performance evaluation, Prediction accuracy
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
