This project is part of 5G Architectures, Technologies, Applications and Key Performance Indicators elective course in Computer Engineering & Informatics Department of University of Patras for Spring Semester 2025 (Semester 10).
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This project involves predicting the downlink bitrate of mobile devices in 5G networks using machine learning (XGBoost Regressor) and deep learning (LSTM model). It includes data preprocessing, training and evaluation of the models, applying explainable AI (XAI) techniques such as SHAP, and optimizing feature selection based on XAI insights.
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miltiadiss/Mobile-device-downlink-bitrate-prediction-in-5G-networks
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This project involves predicting the downlink bitrate of mobile devices in 5G networks using machine learning (XGBoost Regressor) and deep learning (LSTM model). It includes data preprocessing, training and evaluation of the models, applying explainable AI (XAI) techniques such as SHAP, and optimizing feature selection based on XAI insights.
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