Citation: Mozo, A.; Karamchandani,
A.; Gómez-Canaval, S.; Sanz, M.;
Moreno, J.I.; Pastor, A. B5GEMINI:
AI-Driven Network Digital Twin.
Sensors 2022, 22, 4106. https://
doi.org/10.3390/s22114106
Academic Editors: Alexandros-
Apostolos Boulogeorgos, Panagiotis
Sarigiannidis, Thomas Lagkas,
Vasileios Argyriou and Pantelis
Angelidis
Received: 7 May 2022
Accepted: 24 May 2022
Published: 28 May 2022
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Article
B5GEMINI: AI-Driven Network Digital Twin
Alberto Mozo
1,
* , Amit Karamchandani
1
, Sandra Gómez-Canaval
1
, Mario Sanz
2
, Jose Ignacio Moreno
2
and Antonio Pastor
1,3
1
ETSI Sistemas Informáticos, Dpto. Sistemas Informáticos, Universidad Politécnica de Madrid,
28031 Madrid, Spain; amit.kbatra@alumnos.upm.es (A.K.); sm.gomez@upm.es (S.G.-C.);
antonio.pastorperales@telefonica.com (A.P.)
2
ETSI Telecomunicación, Dpto. Ingeniería de Sistemas Telemáticos, Universidad Politécnica de Madrid,
28040 Madrid, Spain; mario.sanz@upm.es (M.S.); joseignacio.moreno@upm.es (J.I.M.)
3
Telefónica I+D., 28050 Madrid, Spain
* Correspondence: a.mozo@upm.es
Abstract:
Network Digital Twin (NDT) is a new technology that builds on the concept of Digital
Twins (DT) to create a virtual representation of the physical objects of a telecommunications network.
NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts
while eliminating the need to directly interact with them. There is broad consensus that Artificial
Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this
work, we present B5GEMINI, which is an NDT for 5G and beyond networks that makes an extensive
use of AI and ML. First, we present the infrastructural and architectural components that support
B5GEMINI. Next, we explore four paradigmatic applications where AI/ML can leverage B5GEMINI
for building new AI-powered applications. In addition, we identify the main components of the AI
ecosystem of B5GEMINI, outlining emerging research trends and identifying the open challenges
that must be solved along the way. Finally, we present two relevant use cases in the application of
NDTs with an extensive use of ML. The first use case lays in the cybersecurity domain and proposes
the use of B5GEMINI to facilitate the design of ML-based attack detectors and the second addresses
the design of energy efficient ML components and introduces the modular development of NDTs
adopting the Digital Map concept as a novelty.
Keywords:
digital twin; network digital twin; artificial intelligence; machine learning; telecommunications
1. Introduction
The concept and development of the Digital Twin (DT) was first formulated by Grieves
and Vickers [
1
]. According to that original definition, a DT is a virtual model that resembles
the characteristics and behavior of a physical asset or system, modeling its components
and properties, as well as the interactions of the entity with the environment. A DT can be
used to monitor and manage a physical asset or system in real-time or use it to recreate its
behavior in predefined virtual scenarios in order to define a better industrial process or
detect problems before the real implementation.
The application of the DT concept in the field of communication networks has also
recently gained attention in both the research community and the industrial sector, leading
to the emergence of the Network Digital Twin (NDT) concept. An NDT is a virtual represen-
tation of a telecommunications network that accurately models the devices, communication
links, operating environment, and applications running on the network. NDTs are a new
way of designing and managing networks, in which multiple physical assets and their
corresponding virtual twins are connected together to share information and collaborate
to complete a set of tasks [
2
]. In NDTs, network operators in coordination with service
providers and telecommunication equipment suppliers can validate new functionalities
prior to their incorporation into the network or emulate existing situations in the network
and analyze the effect of applying different policies in the network.
Sensors 2022, 22, 4106. https://doi.org/10.3390/s22114106 https://www.mdpi.com/journal/sensors
