Common use of Problem and challenges Clause in Contracts

Problem and challenges. B5G network opens new opportunities to operators to apply ML to solve multiple problems, including advanced security management. To achieve these results, it is needed to invest a non-negligible quantity of effort in the data engineering process, including data sources identification, data transformation, and to evaluate conditions such as frequency and quality of the data. Network range- digital twin (NDT) appears as a potential solution for assessing solutions related to AI/ML architectures. This includes generation, collection, and transformation of data to design and test different ML models in an emulated environment before deploying in production, reducing the cost and investment. This model could fit for offline ML training, and ML inference engine delivery. Figure 18 shows the holistic process combining several enablers. Mouseworld acts as the 5G twin environment for network traffic generation and delivery. This network flows related information is collected and aggregated through a data collector. The output generated is delivered to design an ML model. The outcome is integrated into the Smart Traffic Analyzer and validated in the Mouseworld. If the model does not achieve the expectations or the traffic scenarios evolves, a redesign can be done.

Problem and challenges. B5G network opens new opportunities to operators to apply ML to solve multiple problems, including advanced security management. To achieve these results, it is needed to invest a non-negligible quantity of effort efforts in the data engineering process, including data sources identification, data transformation, and to evaluate conditions such as frequency and quality of the data. Network range- digital twin (NDT) appears as a potential solution for assessing solutions related to AI/ML architectures. This includes generationgenerate, collectioncollect, and transformation of transform data to design and test different ML models in an emulated environment before deploying in production, reducing the cost and investment. This model could fit for offline ML training, and ML inference engine delivery. Figure 18 shows the holistic process combining several enablers. Mouseworld acts as the 5G twin environment for network traffic generation and delivery. This network flows related information is collected and aggregated through a data collector. The output generated is delivered to design an ML model. The outcome is integrated into the Smart Traffic Analyzer and validated in the Mouseworld. If the model does not achieve the expectations or the traffic scenarios evolves, a redesign can be done.