Synthetic Wireless Network Dataset for AI-Enhanced Predictive Analytics in Next-Generation Network Simulations

This dataset was developed in support of the research project titled “Integrating Artificial Intelligence into Network Simulation for Enhanced Predictive Analytics.” The project focuses on improving network monitoring and outage prediction by incorporating AI techniques into simulated wireless environments. To facilitate this, synthetic data was generated using MATLAB to model a multi-user wireless network under varying signal conditions and resource constraints.

The simulation captures key communication metrics such as distance from the transmitter, received signal strength (RSSI), signal-to-noise ratio (SNR), and time steps representing different moments in network activity. These variables are intended to reflect realistic fluctuations in signal behavior, making the dataset useful for evaluating predictive analytics models.

Two versions of the dataset are included. The first file, Simulated_WirelessMetrics_Step1 (available in both .csv and .xlsx formats), contains the raw synthetic wireless data without labels. The second file, LabeledWirelessData_Step2, includes the same features as the first but with an added binary label “Outage” that indicates whether a communication failure occurred at a given point. This labeling was based on predefined thresholds for RSSI and SNR and is suitable for supervised learning tasks such as classification or anomaly detection.

The dataset can be used for training and evaluating machine learning models, benchmarking AI-assisted simulations, or exploring how signal conditions impact network performance. Both versions are provided in CSV and Excel formats to accommodate different tools and user preferences.