Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology to enhance the performance of next-generation wireless networks, particularly in terms of increasing coverage and enhancing user throughput. However, effective radio resource management for RIS-aided mobile networks introduces significant complexity due to the high dimensionality and the interdependency of these tasks.

This thesis proposes a solution that leverages the machine learning-based framework of Graph Neural Networks (GNNs) to jointly optimize radio resource management tasks in a multi-user scenario.
The proposed model is capable of performing user scheduling, beamforming, and RIS configuration based on full channel information that maximizes proportional fairness. Additionally, the model also supports implicit power allocation.

A comprehensive simulation environment emulating a dense urban mobile network is developed to train and test the model. The performance across various mobile network deployments is evaluated and compared to an accurate existing method.
Results demonstrate that the proposed GNN-based solution manages to achieve a large fraction of the user throughput gain achieved using the existing method in significantly less computation time, showcasing its potential for real-time radio resource management in future 6G networks.