README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - telecommunications
  - mobile
  - internet
  - 4G
pretty_name: Synthetic Mobile Performance Dataset
size_categories:
  - 10M<n<100M

Dataset Card: Synthetic Mobile Network Performance

Dataset Description

This dataset contains synthetically generated mobile signal measurements designed to mirror real-world data from Vodafone's NetPerform system in the UK. The data represents geolocated signal quality metrics from mobile devices, capturing a range of environmental and temporal conditions over several months in 2025.

The source data is collected via an SDK embedded in user applications, which passively records network connectivity parameters. This provides a user-centric view of mobile network performance. All data has been anonymized, aggregated, and processed to protect user privacy. The synthetic dataset has undergone pre-processing, including coordinate validation and outlier removal.

Fields

The dataset contains the following columns:

• timestamp: (date) The date and time of the measurement, recorded in UTC.
• unique_cell: (string) A unique identifier for the mobile network cell to which the device was connected.
• measurement_type_name: (string) The type of measurement recorded. This column appears to be null in the provided sample.
• in_outdoor_state: (string) A categorical label indicating the predicted environment of the device, with values such as "Surely Indoor," "Probably Indoor," and "Surely Outdoor."
• value: (float) A numerical value associated with the measurement. The specific KPI this represents is not defined.
• latitude: (float) The latitude of the device's location at the time of measurement.
• longitude: (float) The longitude of the device's location at the time of measurement.
• signal_level: (float) The received signal strength, likely measured in dBm.

Potential Uses

This dataset is suitable for a variety of analyses, including:

• Network Coverage Mapping: Visualizing signal strength across different geographic areas to identify zones with strong or weak coverage.
• Performance Analysis: Correlating signal quality with factors like location (indoor/outdoor), time of day, and cell tower.
• Mobility Pattern Simulation: Understanding how user movement impacts network performance.
• Machine Learning Model Training: Developing models to predict signal quality based on location and environmental factors.