Graph neural network based handover optimization framework

In the evolving landscape of mobile networks, an innovative handover optimization framework for next-generation networks in Open Radio Access Network settings is presented in this thesis. The research, focusing on embedding mobile networks including user equipments and base stations to better capture the network dynamics in handover decisioning, is facilitated through the utilization of Graph Neural Network (GNN) based framework. The core objective is to optimize critical aspects such as load balancing, handover cost, throughput gain, and coverage gain. This framework, called GNN-HOF (Graph Neural Network Based Handover Optimization Framework), is a significant departure from traditional proximity-based methods, leveraging advanced machine learning techniques to better understand and predict network dynamics. The efficacy of the approach is validated through extensive testing in simulated environments as well as real-world urban scenarios in Stuttgart and Monaco using the Simulation of Urban Mobility (SUMO) tool. The results are compelling, demonstrating that the proposed framework consistently outperforms the baseline method across all key metrics.