Name
Title | ||
|---|---|---|
Implementation of Machine Learning for Autonomic Capabilities in Self-Organizing Heterogeneous 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11277-016-3843-2 | Wireless Personal Communications |
Keywords | Field | DocType |
Self-organizing networks,Machine learning,Energy saving,Self-managing network | Abstraction,Computer science,Autonomic networking,Self-organizing network,Artificial intelligence,Heterogeneous network,Computational learning theory,Optimization problem,Modularity,Reusability,Machine learning | Journal |
Volume | Issue | ISSN |
92 | 1 | 0929-6212 |
Citations | PageRank | References |
2 | 0.55 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| plamen semov | 1 | 8 | 3.44 |
| Hussein Al-Shatri | 2 | 105 | 14.40 |
| Krasimir Tonchev | 3 | 10 | 8.51 |
| Vladimir Poulkov | 4 | 56 | 20.39 |
| Anja Klein | 5 | 173 | 89.48 |