  The next-generation wireless networks are evolving into very complex systems
because of the very diversified service requirements, heterogeneity in
applications, devices, and networks. The mobile network operators (MNOs) need
to make the best use of the available resources, for example, power, spectrum,
as well as infrastructures. Traditional networking approaches, i.e., reactive,
centrally-managed, one-size-fits-all approaches and conventional data analysis
tools that have limited capability (space and time) are not competent anymore
and cannot satisfy and serve that future complex networks in terms of operation
and optimization in a cost-effective way. A novel paradigm of proactive,
self-aware, self- adaptive and predictive networking is much needed. The MNOs
have access to large amounts of data, especially from the network and the
subscribers. Systematic exploitation of the big data greatly helps in making
the network smart, intelligent and facilitates cost-effective operation and
optimization. In view of this, we consider a data-driven next-generation
wireless network model, where the MNOs employ advanced data analytics for their
networks. We discuss the data sources and strong drivers for the adoption of
the data analytics and the role of machine learning, artificial intelligence in
making the network intelligent in terms of being self-aware, self-adaptive,
proactive and prescriptive. A set of network design and optimization schemes
are presented with respect to data analytics. The paper is concluded with a
discussion of challenges and benefits of adopting big data analytics and
artificial intelligence in the next-generation communication system.
