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

X-GRL: An Empirical Assessment of Explainable GNN-DRL in B5G/6G Networks