Next generation self-organizing networks (NGSONs)
are the key that will lead to the full automation of
the network management in the forthcoming generations of
cellular communications. New challenges, like the deployment
of novel wireless services or the aim of operators at providing
an end-to-end monitoring and optimization, make it necessary
to develop an innovative scheme for network management. In
this paper, a self-healing (SH) framework for next-generation
networks using dimensionality reduction is proposed as the tool
enabling the management of an increasingly complex network,
taking advantage of both feature selection and feature extraction
techniques. A proof of concept has been carried out in the context
of automatic diagnosis in a live network. Results show that the
proposed framework can effectively manage a high-dimensional
environment, eventually automating the tasks usually performed
by troubleshooting experts while optimizing the performance of
the diagnosis tool.
