# B5 GEMINI: AI-Driven Network Digital Twin
## 2 Related Work
This can be important for organizations that need to maintain the confidentiality of their data or have limited resources. Leveraging this concept, [PERSON] et al. build their architecture based on this scheme to alleviate privacy concerns in NDT, while reducing data transfer overhead. To minimize energy consumption and reduce data transfers in a Mobile Edge Computing (MEC) scenario, [PERSON] et al. propose an optimization of resource allocation and data offloading probabilities to the cloud, as well as an efficient mapping of the association between users and edge devices, by approximating these strategies using DL models in a supervised manner [12]. For this purpose, an NDT of the communication network is constructed and deployed on a central server, different configurations are evaluated in the NDT, and the best ones are used to train a DNN model in an off-line fashion. [PERSON] et al. describes the key technologies that will support the deployment and operation of an NDT in an industrial setting, including edge and fog computing, and a 5G network [13]. Additionally, they determine the role of AI methods to address some of the challenges posed in this scenario. More interestingly, they identify AI as a tool to optimally allocate and automatically scale computing resources for an NDT to satisfy key performance indicators (KPIs) such as latency and security requirements. In addition, they conduct an experimental evaluation as a proof-of-concept to predict the movement of a robotic arm using data collected from a DT to train a variety of ML models that are subsequently compared. A vehicular network (VN) is a collection of nodes that are connected by links. Nodes can be cars, trucks, buses, or other vehicles. The links can be streets, highways, or other transportation infrastructure. A VN can be used to model the movement of vehicles in a city. A software-defined vehicular network (SDVN) is a VN that uses the concept of software-defined networking (SDN) to allow network administrators to manage network traffic through software defined rules instead of hardware configurations [14]. This enables large networks to be managed more efficiently and to respond quickly to changing network conditions. Based on this concept, [PERSON] et al. propose to create an NDT of a SDVN as a simulation environment to evaluate the quality and reliability of ML and DL models before applying them in the physical network [15]. This simulations environment, which they call the \"Intelligent Digital Twin\" (IDT), allows one to assess whether the models remain robust to the intrinsic dynamic changes of a VN and retrains the models if needed. A similar use case is presented in [3]. In this paper, the authors identify 5G as a key technology to enable efficient inter-vehicle communication to satisfy the necessary low latency and high throughput requirements that are critical requirements to achieve autonomous driving. In this particular context, with the objective of improving road safety and traffic management, the [PERSON] team proposes to achieve this goal by creating a Digital Twin of a 5G SDVN to emulate all aspects of a 5G network and its interactions with vehicles under different realistic scenarios (e.g., congestion and vehicle density) in a controlled manner [16]. In this approach, AI methods can be introduced to understand and evaluate vehicle behavior, optimize vehicle-to-vehicle communication, and enable end-to-end validation of the entire 5G SDVN. In this way, the built NDT serves as a testbed to verify vehicle performance prior to deployment in a real-world environment. Similar to the above presented case, in 2016, General Electric filed a US patent for a wind farm management solution that leverages the concept of NDT to facilitate the operations and test diverse strategies in a simulation environment powered by AI [17].