# B5 GEMINI: AI-Driven Network Digital Twin
## 1 Introduction  
The concept and development of the Digital Twin (DT) was first formulated by [PERSON] and [PERSON] [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 representation 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. By leveraging network data, NDTs can be used to build a virtual representation of a network. The data should replicate the expected behavior of the network; for this reason, the most common source are live feeds from the real network, although data collected from testbeds or simulations that reflect the intended behavior can also be used. By replicating different environments in a lab and running multiple scenarios, NDTs offer a cost-effective way to assess performance, predict the impact of environmental changes (such as cyber threats), and optimize network processes and decision making accordingly. Although NDTs are still in their nascent stage, it is envisioned that they will have a pervasive impact on the telecommunication industry, and they are pictured to be a keystone technology for the fourth industrial revolution. The pursue of this interest has led to an accelerated research effort in the last years, mainly because they can be used to thoroughly dissect the response of a network under different conditions and test new networking designs without compromising the safety of the physical network. Additionally, by creating an NDT, the real time optimization of its corresponding physical equivalent is also possible. Furthermore, NDTs can help secure traditional networks by enabling fast identification and isolation of network failures to quickly respond to security threats. An NDT can be interpreted as a black-box model of a network that can be parameterized in multiple ways to recreate different network scenarios (e.g., different topologies, traffic volume, dynamic placement policies); by adjusting the parameter values, it is possible to observe the resulting changes in the NDT in real time and evaluate the situation using different metrics defined according to the operator's needs. More interestingly, the above is possible without disrupting the real network or incurring the construction of costly test environments. In search of mechanisms to improve network intelligence, the use of artificial intelligence (AI) for the creation and operation of NDTs has begun to be explored in recent years. In fact, there is broad consensus that AI and ML are among the key enablers of the NDT concept. AI methods can be used to improve the accuracy of NDT simulations and to enable new services that ultimately improve user experience, such as self-healing networks, autonomous network management and security monitoring.