An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing

TL;DR

This paper presents an explainable deep reinforcement learning framework for dynamic resource management and network slicing in vehicular networks, enhancing interpretability and QoS for diverse service types.

Contribution

It introduces an innovative XRL framework that combines feature-based explanations with attention mechanisms for improved decision interpretability in vehicular network slicing.

Findings

Increased QoS satisfaction for URLLC from 78.0% to 80.13%.

Enhanced eMBB QoS from 71.44% to 73.21%.

Achieved higher interpretability precision than pure attention mechanisms.

Abstract

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%.