# B5 GEMINI: AI-Driven Network Digital Twin
## 1 Introduction
In addition, AI methods can also be used to automatically learn and adapt to changes in real assets. This can help ensure that nondestructive testing remains accurate over time. Communication networks often work with large amounts of heterogeneous data that differ greatly in format and content. For this reason, creating a model of the network that accurately represents its behavior is a highly challenging task. Fortunately, Machine Learning (ML) techniques can help to extract insight from the real network data and construct the corresponding NDT of the network automatically [3]. Furthermore, the increasing excitement generated by the NDT concept in the last few years has raised interest in the NMRG IETF standardization group. The NMRG group has recently published a draft that proposes a reference framework for NDTs and their possible applications [4]. Among these applications, ML model training and validation is explicitly mentioned. It should also be emphasized that one of the main concepts in this draft is the recommendation to have an exchange of information between the real physical network and the DT to obtain maximum benefit. In order to achieve the goals proposed by the IETF draft, in this work, we make the case of ML methods as a key enabler for the construction of an NDT that models complex 5G and beyond networks. To that end, we present in this work B5 GEMINI, a proposal for an AI-based NDT architecture, whose main objective is to deploy a 5G/6G NDT support platform capable of building a specific network configuration and generating the necessary traffic to be subsequently used for different analysis activities focused on advanced scenarios, such as cybersecurity and network management, which range from resource optimization to security enforcement. It should be noted that B5 GEMINI is an acronym for \"Beyond 5G GEMINI\", where the term \"gemini\" corresponds to the Latin word for \"twins\". Due to the inherent complexity of 5G/6G models, B5 GEMINI aims to make extensive use of AI to provide them with anticipatory and self-learning capabilities. B5 GEMINI addresses several complementary objectives: the development of a 5G/6G NDT infrastructure and the design and integration of advanced AI components and use cases in the NDT for realistic experimentation. It is worth noting that current NDT proposals tend to apply AI to add intelligence in different ways to network orchestration processes. B5 GEMINI goes one step beyond and also focuses on the use of NDT for training and validating AI/ML components in a controlled scenario when potentially harmful situations could occur if trained or tested in a real network. B5 GEMINI will provide a virtualized representation of the 5G/6G network meant to analyze, diagnose, emulate, and control the physical network. The virtual system will start with a simple form of the real system and, using AI self-learning capabilities and data updating, evolve gradually to a more realistic image. With the support of AI, complex data sources and telemetry (e.g., IoT sensors, network data) will be fed into sophisticated data interpretation processes to facilitate the replica of 5G/6G network components. In addition, B5 GEMINI will allow for the deploying of complex network scenarios in a controlled way, launching clients and servers and collecting the traffic generated by them even if they interact with clients and servers outside the NDTs, typically on the wider Internet.