1. Introduction

In this use case, we utilize the "Cell Metrics"(RRU.PrbUsedDl) dataset provided by the O-RAN SC SIM space, which includes synthetic data generated by a simulator, with all data recorded in Unix timestamp format.
- Download data: https://nexus3.o-ran-sc.org/repository/datasets/CellReports.csv
The model training process is carried out on the O-RAN SC AI/ML Framework, including GPU support, and considers both traditional machine learning (ML) and deep learning (DL) approaches. For ML models, we use Random Forest and Support Vector Regression (SVR), while for DL models, we employ RNN, LSTM, and GRU architectures.
By managing the ON/OFF state of cells through traffic forecasting, we can reduce power consumption. Additionally, if the AI/ML models used for forecasting are operated in an eco-friendly manner, further power savings can be achieved. In this use case, we measure the carbon emissions and energy consumption during the cell traffic forecasting process using AI/ML to ensure that the forecasting model is not only effective but also environmentally sustainable.

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CellReports.csv
The file contains traffic data from Viavi.
View file
name insert.py
The file processes the dataset and inserts the data into InfluxDB.
(Changed required: DATASET_PATH , INFLUX_IP , INFLUX_TOKEN)
View file
name pipeline.ipynb
The file defines the model structure and training process.
View file
name deploy.yaml
The yaml file is used for deploying model inference service.
View file
name predict.sh
The script used for excuting the model prediction.
View file
name input.json
The json file is used for Inference.

5. Example

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