  The rapid development of artificial intelligence (AI) techniques has
triggered a revolution in beyond fifth-generation (B5G) and upcoming
sixth-generation (6G) mobile networks. Despite these advances, efficient
resource allocation in dynamic and complex networks remains a major challenge.
This paper presents an experimental implementation of deep reinforcement
learning (DRL) enhanced with graph neural networks (GNNs) on a real 5G testbed.
The method addresses the explainability of GNNs by evaluating the importance of
each edge in determining the model's output. The custom sampling functions feed
the data into the proposed GNN-driven Monte Carlo policy gradient (REINFORCE)
agent to optimize the gNodeB (gNB) radio resources according to the specific
traffic demands. The demo demonstrates real-time visualization of network
parameters and superior performance compared to benchmarks.
