Collection, Generation and Validation of Datasets suitable for training AI Models for 6G Networks and for AIaaS

Expected Outcome: The target outcomes include: • A curated collection of high-quality real-world datasets, that have enough quality to train AI models that can run in operations, capturing realistic deployment scenarios—including user density, mobility patterns, network conditions, and realistic data traffic patterns from various applications, including vertical applications (non-exhaustive list). The collection could be obtained by any means or tools indicatively including but not limited to operational networks (real data) and/or network digital twins or advanced (high TRL) experimental platforms (emulators) or trials, gen AI (augmenting field measurements), and be significant in scale and broad representativeness (in terms of technologies, use-cases and verticals) designed to ensure scalability, providing a robust foundation for the training and validation of AI models for 6G networks and for AIaaS. • An open-source simulator to create synthetic data sets (correlated between different network layers, and across different network points) of high quality capturing realistic deployment scenarios—including user density, mobility patterns, network conditions, anomalies and network attacks and realistic data traffic patterns from various applications, including vertical applications, etc. • A modular framework and related methodology for generating and processing (e.g., cleaning, preprocessing) high quality realistic synthetic datasets to train AI models usable by 6G systems. •…