Digital sources have been enabling unprecedented data-driven and large-scale
investigations across a wide range of domains, including demography, sociology …

This paper presents a machine learning-based framework for the predictive deployment of
unmanned aerial vehicles (UAVs) as flying base stations (BSs) to offload heavy traffic from …

Bridging the advantages of differential privacy in both centralized model (ie, high accuracy)
and local model (ie, minimum trust), the shuffle privacy model has potential applications in …

For highly distributed environments such as edge computing, collaborative learning
approaches eschew the dependence on a global, shared model, in favor of models tailored …

Real-time traffic in a cellular network varies over time and often shows tidal patterns, such as
the day/night traffic pattern. With this characteristic, we can reduce the energy consumption …

Many vehicle-to-everything (V2X) applications and use cases require their feasibility to be
simulated, tested, and validated in realistic traffic scenarios and under various network …

Forecasting of traffic in cellular network is a significant service for management of available
resources strategically in an efficient way. Valuable resources such as link bandwidth and …

In contrast to federated learning, collaborative learning aims to reduce data transfer caused
by frequent model updates by creating lightweight tailored models at the edge nodes. If the …

Improving energy efficiency is vital for the 5G mobile telecommunication network, while the
conventional temporal shutdown strategy is exhausting its energy-saving potential. Inspired …

There is an understandable expectation across society that 5G will support Society-Critical
System (SCS) use cases such as connected vehicles and industrial robots. This is based on …