| Autor: |
Giovanni Nardini, Alessandro Noferi, Pietro Ducange, Giovanni Stea |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
SoftwareX, Vol 21, Iss , Pp 101320- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2352-7110 |
| DOI: |
10.1016/j.softx.2023.101320 |
| Popis: |
Researchers working on Artificial Intelligence (AI) need suitable datasets for training and testing their models. When it comes to applications running through a mobile network, these datasets are difficult to obtain, because network operators are hardly willing to expose their network data or to open their network to experimentation. In this paper we show how Simu5G, a popular 5G network simulator based on OMNeT++, can be used to circumvent this problem: it allows users to log data at arbitrary spatial and temporal resolution, belonging to every network layer — from the application to the physical one. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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