Name
Abstract | ||
|---|---|---|
The increasing amount of network elements in the current deployments of cellular networks is leading to an enormous complexity of the operation and maintenance. Self- Organizing Networks (SONs) is a good solution for operators to save operational expenditures by automating network management. One of the key challenges in this context is the automatic identification of degraded cells. In this paper, a method to detect degraded cells through the analysis of the time evolution of metrics is proposed. Results show that cell faulty patterns can be effectively detected by comparing them with a set of fictitious degraded patterns. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LCOMM.2016.2516004 | Communications Letters, IEEE |
Keywords | Field | DocType |
Correlation,Fault Detection,Long-Term Evolution,Self-Healing,Self-Organizing Networks | Time series,Computer science,Degradation (geology),Real-time computing,Correlation,Cellular network,Operator (computer programming),Network element,Network management | Journal |
Volume | Issue | ISSN |
PP | 99 | 1089-7798 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Munoz, P. | 1 | 34 | 1.49 |
| Raquel Barco | 2 | 364 | 41.12 |
| Inmaculada Serrano | 3 | 60 | 9.79 |
| Gomez-Andrades, A. | 4 | 1 | 0.35 |
| Pablo Muñoz Luengo | 5 | 217 | 17.83 |
| Ana Gómez-Andrades | 6 | 1 | 0.35 |