Autors: Semov, P. T., Al-Shatri, H., Tonchev, K., Poulkov, V. K., Klein, A.
Title: Implementation of Machine Learning for Autonomic Capabilities in Self-Organizing Heterogeneous Networks
Keywords: Energy saving; Machine learning; Self-managing network; Self-organizing networks

Abstract: The 3GPP’s self-organizing networks (SONs) standards are a huge step towards the autonomic networking concept. They are the response to the increasing complexity and size of the mobile networks. This paper proposes a novel scheme for SONs. This scheme is based on machine learning techniques and additionally adopting the concept of abstraction and modularity. The implementation of these concepts in a machine learning scheme allows the usage of independent vendor and technology algorithms and reusability of the proposed approach for different optimization tasks in a network. The scheme is tested for solving an energy saving optimization problem in a heterogeneous network. The results from simulation experiments show that such an approach could be an appropriate solution for developing a full self-managing future network.

References

    Issue

    Wireless Personal Communications, vol. 92, issue 1, pp. 149-168, 2017, Switzerland, Springer, DOI 10.1007/s11277-016-3843-2

    Copyright Springer Science+Business Media New York

    Цитирания (Citation/s):
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    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science