Name
Abstract | ||
|---|---|---|
The arrival of the fifth generation (5G) standard has further accelerated the need for operators to improve the network capacity. With this purpose, mobile network topologies with smaller cells are currently being deployed to increase the frequency reuse. In this way, the number of nodes that collect performance data is being further risen, so the number of metrics to be managed and analyzed is being highly increased. Therefore, it is fundamental to have tools that automatically inform the network operator of the relevant information within the vast amount of metrics collected. The continuous monitoring of the performance indicators and the automatic detection of anomalies is especially important for network operators to prevent the network degradation and user complaints. Therefore, this paper proposes a methodology to detect and track anomalies in the mobile networks performance indicators online, i.e., in real time. The feasibility of this system was evaluated with several performance metrics and a real LTE Advanced dataset. In addition, it was also compared with the performances of other state-of-the-art anomaly detection systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20247232 | SENSORS |
Keywords | DocType | Volume |
anomaly detection, network operation, LTE, self-healing | Journal | 20 |
Issue | ISSN | Citations |
24 | 1424-8220 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesús Burgueño | 1 | 1 | 0.35 |
| Isabel de la Bandera | 2 | 105 | 11.13 |
| Jessica Mendoza | 3 | 1 | 0.69 |
| David Palacios | 4 | 2 | 1.39 |
| Cesar Morillas | 5 | 1 | 0.35 |
| Raquel Barco | 6 | 364 | 41.12 |