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Wireless networks are key enablers of ubiquitous communication. With the
evolution of networking technologies and the need for these to inter-operate
and dynamically adapt to user requirements, intelligent networks are the need
of the hour. Use of machine learning techniques allows these networks to adapt
to changing environments and enables them to make decisions while continuing to
learn about their environment. In this paper, we survey the various problems of
wireless networks that have been solved using machine-learning based prediction
techniques and identify additional problems to which prediction can be applied.
We also look at the gaps in the research done in this area till date.
The stability and reliability
of links in wireless networks is dependent on a number of factors such as the
topology of the area, inter-base station or inter-mobile station distances,
weather conditions and so on. Link instability in wireless networks has a
negative impact on the data throughput and thus, the overall quality of user
experience, even in the presence of sufficient bandwidth. An estimation of link
quality and link availability duration can drastically increase the performance
of these networks, allowing the network or applications to take proactive
measures to handle impending disconnections. In this paper we look at a
mathematical model for predicting disconnection in wireless networks. This
model is originally intended to be implemented in base stations of cellular
networks, but is independent of the wireless technology and can thus be applied
to different types of networks with minimum changes.