This repository hosts the source code of and assets related to my Master's Thesis about 5G Handover Optimization w.r.t. Quality of Experience. Here's the abstract:

Cellular handovers ensure the continuous connectivity of mobile devices as they move through various mobile network cells. Handover decisions in mobile networks can significantly impact the Quality of Experience of mobile device users as they influence both the achievable data rate and the power consumption of a mobile device. This thesis optimizes cellular handovers using a machine learning technique (Reinforcement Learning), considering both power consumption and data rates. To quantify power consumption of mobile devices, a power model is developed and validated with power measurements in a commercial mobile network. The model is subsequently integrated into a system-level simulator for cellular handovers and applied to jointly optimize data rates and power consumption of mobile network devices with respect to Quality of Experience. Both the developed model and the handover simulation environment are publicly available and can be applied and extended in future network research.

The repository contains the following projects:

• power-model: A Python implementation of the system-level 5G NR UE power model developed in the thesis
• scripts: All the measurement, plotting, and symbolic computation scripts used for the power model and its measurement-based validation
• simulation: A UE-power-aware, simplified and extended fork of the system-level cellular network simulator mobile-env, plus a bunch of scripts for training and testing Reinforcement Learning agents on the environment.
• thesis: The Typst code of the thesis
• presentation: The Typst code of the thesis presentation