Datasets:
				
			
			
	
			
	
		
			
	
		
		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.
π Dataset Structure
The dataset is organized into three main CSV files:
1. BSinfo.csv β 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 β 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 β 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
π 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
π§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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