---
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 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.

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.