This is the repository for the O-RAN assignment/hands-on related to the tech camp.
You will learn to work with cellular datasets and train AI solutions to classify traffic profiles.
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The script Train_Predict_Light.py offers a baseline training pipeline that shows you how to open the dataset, extract/plot data, and provide a skeleton to train a base AI algorithm.
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The script Test_Performance_Fake.py offers a testing algorithm for your trained algorithm to verify that your solution processes data correctly and can be evaluated.
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The script Train_Predict_Full.ipynb offers a jupyter notebook containing a baseline training pipeline that shows you how to open the dataset, extract/plot data, and provide you an implementation of a traditional training & prediction AI pipeline for two classifiers of example.
The datasets you need to download are located at:
- Fake testing dataset available at this link. This has the same format as the testing dataset but has not the same data that will be used to evaluate your algorithms :) We will make this dataset available once you are all done with the activities.
- Real testing dataset available at this link.
- Training dataset available at this link. This is the training dataset you should use. We suggest you split this into 3 subsets for training, validation and testing. Note that the testing dataset you will produce here is different from the one above.
Important:
- We will ask you to submit a trained model we can test using the
./Test_Performance_Fake.py
script. If you cannot test your model using that script, it means that you need to change input/output format to match the requirement. - Groups that are willing to present their approach, are free to prepare 2-3 slides to present their work and results to the audience. Please let us know in advance if you are willing to present.
Slides from the talk:
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ML Primer: this link
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Detaset description slides: this link
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Original dataset available at this link
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Paper describing the dataset and AI-based xApps is M. Polese, L. Bonati, S. D'Oro, S. Basagni, T. Melodia, "ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms," IEEE Transactions on Mobile Computing, pp. 1-14, July 2022. PDF
**Email contacts
- Andrea Pimpinella: [email protected]
- Salvatore D'Oro: [email protected]