SliceVision-F2I: A Synthetic Feature-to-Image Dataset for Visual Pattern Representation on Network Slices

The article presents SliceVision-F2I, a newly developed synthetic dataset aimed at enhancing feature visualization within network slicing, a critical component for advancing 5G and 6G network technologies. This dataset is designed to improve methods for identifying visual patterns in network slices, thereby supporting the development of future service-oriented architectures. By providing a structured resource for visual pattern representation, SliceVision-F2I addresses challenges in analyzing complex network slice features. The dataset’s application domain is specifically focused on telecommunications networks, where efficient slicing is essential for optimizing network performance and resource allocation. As 5G and emerging 6G networks increasingly rely on sophisticated slicing techniques, tools like SliceVision-F2I are expected to play a significant role in research and development. This contribution aligns with ongoing efforts to create datasets that facilitate machine learning and artificial intelligence applications in network management. Overall, SliceVision-F2I represents a step forward in enabling more effective visualization and interpretation of network slice features.