Exploiting Simu5G for generating datasets for training and testing AI models for 5G/6G network applications
Giovanni Nardini,
Alessandro Noferi,
Pietro Ducange,
Giovanni Stea
Affiliations
Giovanni Nardini
Dipartimento di Ingegneria dell’Informazione, University of Pisa, Largo L. Lazzarino 1, 56122 Pisa, Italy; Center for Logistic Systems, University of Pisa, Via dei Pensieri 60, 57142, Livorno, Italy; Corresponding author at: Dipartimento di Ingegneria dell’Informazione, University of Pisa, Largo L. Lazzarino 1, 56122 Pisa, Italy.
Alessandro Noferi
Dipartimento di Ingegneria dell’Informazione, University of Pisa, Largo L. Lazzarino 1, 56122 Pisa, Italy
Pietro Ducange
Dipartimento di Ingegneria dell’Informazione, University of Pisa, Largo L. Lazzarino 1, 56122 Pisa, Italy
Giovanni Stea
Dipartimento di Ingegneria dell’Informazione, University of Pisa, Largo L. Lazzarino 1, 56122 Pisa, Italy
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.
