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5G Network Energy Consumption Dataset

This dataset provides normalized real-world measurements of energy consumption and operational data from a large-scale 5G network deployment. It includes eight days of measurements collected from more than 1,000 RRU/AAUs, covering 12 different hardware products.

The dataset is intended to support research in energy-efficient mobile networks, network optimization, and data-driven modeling of 5G systems.


πŸ“‚ Dataset Structure

The dataset is organized into three main CSV files:

1. BSinfo.csv – Base Station Basic Information

Contains static information about the deployed base stations and their cells.

Fields:

  • BS: Identifier of the base station
  • CellName: Cell identifier (multiple cells can be configured per BS, named CellX)
  • RUType: Radio unit product name
  • Mode: Transmission mode
  • Bandwidth: Normalized cell bandwidth
  • Frequency: Normalized cell frequency
  • Antennas: Number of antennas
  • TXpower: Maximum transmit power of the cell

2. CLstat.csv – Cell-Level Statistics

Contains hourly counters describing the operational status of each cell.

Fields:

  • Time: Timestamp of the measurement
  • BS: Identifier of the base station
  • CellName: Cell identifier
  • Load: Load of the cell (share of used resources)
  • EnergySavingMode: Intensity of activation of energy-saving mechanisms

3. ECstat.csv – Energy Consumption

Contains hourly measurements of energy consumption per base station.

Fields:

  • Time: Timestamp of the measurement
  • BS: Identifier of the base station
  • Energy: Energy consumption of the base station

πŸ“Š Data Summary

  • Measurement period: 8 days
  • Number of RRUs/AAUs: > 1,000
  • Hardware diversity: 12 different product types
  • Granularity: Hour-level measurements for load, counters, and energy consumption

πŸ§ͺ Applications

This dataset can be used for:

  • Analysis of energy consumption patterns in 5G networks
  • Evaluation of energy-saving methods and load-dependent consumption
  • Development of predictive models for network optimization
  • Benchmarking of AI/ML approaches for green networking

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