Bi‐LSTM model with time distribution for bandwidth prediction in mobile networks

• Published in: ETRI journal Vol. 46; no. 2; pp. 205 - 217
• Main Authors: Lee, Hyeonji, Kang, Yoohwa, Gwak, Minju, An, Donghyeok
• Format: Journal Article
• Language: English
• Published: Electronics and Telecommunications Research Institute (ETRI) 01.04.2024; 한국전자통신연구원
• Subjects: bandwidth prediction; deep learning; handover detection; LSTM; network recognition; 전자/정보통신공학
• ISSN: 1225-6463; 2233-7326
• DOI: 10.4218/etrij.2022-0459

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%.