With the significant increase in mobile users connected to the wireless network, coupled with
the escalating energy consumption and the risk of network saturation, the search for …

Wireless traffic prediction can help operators accurately predict the usage of wireless
networks, and it plays an important role in the load balancing and energy saving of base …

Wireless traffic prediction plays an indispensable role in cellular networks to achieve
proactive adaptation for communication systems. Along this line, Federated Learning (FL) …

Accurately predicting traffic in a cellular network is challenging since the traffic time series
integrated by various wireless services is non-stationary and reveals concealed spatial …

The classic example of machine learning is based on isolated learning—a single model for
each task using a single dataset. Most deep learning methods require a significant amount …

Federated Learning (FL), a crucial advancement in smart city technology, combines real-
time traffic predictions with the potential to enhance urban mobility. This paper suggests a …

This study focuses on the field of cellular traffic prediction and proposes a federated learning
method for cellular traffic prediction based on multi-time scale information combined with …

The advent of 5G technology promises a paradigm shift in the realm of telecommunications,
offering unprecedented speeds and connectivity. However, the efficient management of …

Wireless traffic prediction is critical for cellular networks to achieve intelligent network
operation, and it is well known that the adoption of traffic prediction can facilitate resource …

Link prediction in wireless networks plays a crucial role in predicting missing connections
within multiplex networks. This study focuses on the utilization of similarity-based link …