Name
Title | ||
|---|---|---|
Few-shot Learning for eNodeB Performance Metric Analysis for Service Level Assurance in LTE Networks. |
Abstract | ||
|---|---|---|
With the increasing network topology complexity and continuous evolution of the new wireless technology, it is increasingly challenging to address the network service outage with traditional methods. In order to improve the network operation efficiency, a suitable machine learning technique is used to learn and classify individual base station into different issue based on multiple performance metrics during a specific time window. However an issue classification with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate data. To mitigate the cost and time issues, we propose a method based on few-shot learning that uses Prototypical Networks algorithm to complement the eNodeB states analysis. Using dataset from a live LTE network consists of thousand of eNodeB, our experiment results show that the proposed technique provides high performance while using a low number of labeled data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/NOMS47738.2020.9110296 | NOMS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shogo Aoki | 1 | 0 | 0.34 |
| Kohei Shiomoto | 2 | 0 | 0.34 |
| Chin Lam Eng | 3 | 0 | 0.68 |
| Sebastian Backstad | 4 | 0 | 0.68 |