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

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%.

Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video

“…The authors then utilized a machine-learning-based prediction framework to identify important features and forecast link bandwidth. In [24], the authors predicted the bandwidth, firstly recognizing the type of network to which a UE was connected: 3G, 4G, or 5G. They also used a handover detection algorithm, which caused the bandwidth variance.…”