An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing
It addresses reliability challenges in vehicular communication systems for network operators, but is incremental as it builds on existing methods with added explainability.
This paper tackled dynamic resource management in vehicular network slicing by introducing an Explainable Deep Reinforcement Learning framework, which improved QoS satisfaction for URLLC services from 78.0% to 80.13% and for eMBB services from 71.44% to 73.21%.
Effective resource management and network slicing are essential to meet the diverse service demands of vehicular networks, including Enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC). This paper introduces an Explainable Deep Reinforcement Learning (XRL) framework for dynamic network slicing and resource allocation in vehicular networks, built upon a near-real-time RAN intelligent controller. By integrating a feature-based approach that leverages Shapley values and an attention mechanism, we interpret and refine the decisions of our reinforcementlearning agents, addressing key reliability challenges in vehicular communication systems. Simulation results demonstrate that our approach provides clear, real-time insights into the resource allocation process and achieves higher interpretability precision than a pure attention mechanism. Furthermore, the Quality of Service (QoS) satisfaction for URLLC services increased from 78.0% to 80.13%, while that for eMBB services improved from 71.44% to 73.21%.