  The next generation of mobile networks is set to become increasingly complex,
as these struggle to accommodate tremendous data traffic demands generated by
ever-more connected devices that have diverse performance requirements in terms
of throughput, latency, and reliability. This makes monitoring and managing the
multitude of network elements intractable with existing tools and impractical
for traditional machine learning algorithms that rely on hand-crafted feature
engineering. In this context, embedding machine intelligence into mobile
networks becomes necessary, as this enables systematic mining of valuable
information from mobile big data and automatically uncovering correlations that
would otherwise have been too difficult to extract by human experts. In
particular, deep learning based solutions can automatically extract features
from raw data, without human expertise. The performance of artificial
intelligence (AI) has achieved in other domains draws unprecedented interest
from both academia and industry in employing deep learning approaches to
address technical challenges in mobile networks. This thesis attacks important
problems in the mobile networking area from various perspectives by harnessing
recent advances in deep neural networks.
