New generations of cellular networks are data oriented, targeting the integration of machine
learning and artificial intelligence solutions. Data availability, required to train and compare …

In a world of open data and large-scale measurements, it is often feasible to obtain a real-
world trace to fit to one's research problem. Feasible, however, does not imply simple …

While cellular networks connect over 3.7 billion people worldwide, their availability and
quality is not uniform across regions. Under-provisioned and overloaded networks lead to …

MobileInsight is a software tool that collects, analyzes and exploits runtime, fine-grained
cellular network information over commodity phones. It is our first step to help developers …

The throughput of a cellular network depends on a number of factors such as radio
technology, limitations of device hardware (eg, chipsets, antennae), physical layer effects …

In this paper we use real data to investigate the correlation in time and space between
vehicular traffic and telecom traffic in cellular networks. The analysis of such a correlation is …

Big Data is an arising idea that describes innovative methods and also modern technologies
to assess big volume of complicated datasets that are greatly created from numerous …

In this paper, a survey of the literature of the past 15 years involving machine learning (ML)
algorithms applied to self-organizing cellular networks is performed. In order for future …

Internet access through cellular networks is rapidly growing, driven by the great success of
the mobile apps paradigm and the overwhelming popularity of social-related multimedia …

Hundreds of millions of people still live beyond the coverage of basic mobile connectivity,
primarily in rural areas with low population density. Mobile-network operators (MNOs) …