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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 1/2017

24.10.2016

Implementation of Machine Learning for Autonomic Capabilities in Self-Organizing Heterogeneous Networks

verfasst von: Plamen Semov, Hussein Al-Shatri, Krasimir Tonchev, Vladimir Poulkov, Anja Klein

Erschienen in: Wireless Personal Communications | Ausgabe 1/2017

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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.
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Metadaten
Titel
Implementation of Machine Learning for Autonomic Capabilities in Self-Organizing Heterogeneous Networks
verfasst von
Plamen Semov
Hussein Al-Shatri
Krasimir Tonchev
Vladimir Poulkov
Anja Klein
Publikationsdatum
24.10.2016
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 1/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-016-3843-2

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