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

2025-06-13

AI Summary

To address the challenge of dynamic resource management for eMBB and URLLC services in vehicular network slicing, this paper proposes an explainable deep reinforcement learning (XRL) framework leveraging a near-real-time RAN Intelligent Controller (RIC). The method innovatively integrates Shapley values with a self-attention mechanism to enhance attribution accuracy while preserving decision latency constraints, thereby enabling high-fidelity, interpretable resource scheduling. The framework supports end-to-end slice orchestration and adaptive resource allocation under stringent QoS requirements. Experimental results demonstrate significant improvements in service assurance: URLLC QoS compliance reaches 80.13% (+2.13 percentage points), and eMBB achieves 73.21% (+1.77 percentage points). Moreover, the proposed XRL attains superior explanation fidelity compared to pure attention-based baselines, enabling real-time decision traceability and policy validation.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Reinforcement Learning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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%.

Problem

• Dynamic resource allocation in vehicular network slicing
• Explainable AI for reliable vehicular communication decisions
• Improving QoS for eMBB and URLLC services

Innovation

• Explainable Deep Reinforcement Learning for network slicing
• Shapley values and attention mechanism integration
• Near-real-time RAN intelligent controller framework