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 GPU_ 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.

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