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

TL;DR

SliceVision-F2I is a synthetic dataset transforming network KPI vectors into visual representations using four encoding methods, supporting research in network slicing analysis, anomaly detection, and image-based machine learning.

Contribution

The paper introduces a novel synthetic dataset that converts network KPIs into images via multiple encoding techniques, facilitating visual learning and benchmarking in network slicing research.

Findings

Generated 120,000 samples with diverse encoding methods.

Simulated realistic noisy network conditions.

Supports various tasks like classification and anomaly detection.

Abstract

The emergence of 5G and 6G networks has established network slicing as a significant part of future service-oriented architectures, demanding refined identification methods supported by robust datasets. The article presents SliceVision-F2I, a dataset of synthetic samples for studying feature visualization in network slicing for next-generation networking systems. The dataset transforms multivariate Key Performance Indicator (KPI) vectors into visual representations through four distinct encoding methods: physically inspired mappings, Perlin noise, neural wallpapering, and fractal branching. For each encoding method, 30,000 samples are generated, each comprising a raw KPI vector and a corresponding RGB image at low-resolution pixels. The dataset simulates realistic and noisy network conditions to reflect operational uncertainties and measurement imperfections. SliceVision-F2I is suitable for tasks involving visual learning, network state classification, anomaly detection, and benchmarking of image-based machine learning techniques applied to network data. The dataset is publicly available and can be reused in various research contexts, including multivariate time series analysis, synthetic data generation, and feature-to-image transformations.