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

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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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Authors and Affiliations

  1. Faculty of Telecommunications, Technical University of Sofia, Sofia, Bulgaria

    Plamen Semov, Krasimir Tonchev & Vladimir Poulkov

  2. Institute for Telecommunications, Technical University Darmstadt, Darmstadt, Germany

    Hussein Al-Shatri & Anja Klein

Authors
  1. Plamen Semov
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  2. Hussein Al-Shatri
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  3. Krasimir Tonchev
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  4. Vladimir Poulkov
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  5. Anja Klein
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Corresponding author

Correspondence to Vladimir Poulkov.

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Semov, P., Al-Shatri, H., Tonchev, K. et al. Implementation of Machine Learning for Autonomic Capabilities in Self-Organizing Heterogeneous Networks. Wireless Pers Commun 92, 149–168 (2017). https://doi.org/10.1007/s11277-016-3843-2

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  • DOI: https://doi.org/10.1007/s11277-016-3843-2

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