Spatio-temporal cellular network traffic prediction at wide-area level plays an important role
in resource reconfiguration, traffic scheduling and intrusion detection, thus potentially …

Cellular traffic prediction will play a key role in the deployment of future smart cities.
Although the current traffic prediction methods based on deep learning show better …

Accurate cellular traffic prediction is challenging due to the complex spatial topology of
cellular network and the dynamic temporal feature of mobile traffic. To overcome these …

The past 10 years have witnessed the rapid growth of global mobile cellular traffic demands
due to the popularity of mobile devices. While accurate traffic prediction becomes extremely …

Timely and accurate cellular traffic prediction is difficult to achieve due to the complex spatial-
temporal characteristics of cellular traffic. The latest approaches mainly aim to model local …

Wireless cellular traffic prediction is a critical issue for researchers and practitioners in the
5G/B5G field. However, it is very challenging since the wireless cellular traffic usually shows …

Predicting the future dynamicity of the network traffic are crucially important to support the 5G
intelligent system and automated network management. In this letter, we propose a …

The rapid evolution of cellular technologies has resulted in a drastic increase in mobile data
traffic. Particularly, in 5G cellular networks, the design of accurate time-series models …

Recurrent neural network (RNN) based models are widely adopted to capture temporal
dependencies in the state-of-the-art approaches for cellular traffic prediction. However, RNN …

Cellular traffic prediction plays an important role in network management and resource
utilization. However, due to the high nonlinearity and dynamic spatial-temporal correlation, it …