用可解释强化学习提升车载网络切片的资源分配透明度与性能
An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing
- 结合沙普利值与注意力机制，让强化学习决策过程可解释
- URLLC服务QoS达标率从78.0%提升至80.13%，eMBB从71.44%升至73.21%
- 适合关注车联网可靠性与决策透明性的研究人员和工程师
为满足车载网络中增强移动宽带（eMBB）和超可靠低时延通信（URLLC）的多样化服务需求，有效资源管理与网络切片至关重要。本文提出一种基于近实时无线接入网智能控制器的可解释深度强化学习（XRL）框架，用于动态网络切片与资源分配。通过融合基于特征的沙普利值方法与注意力机制，解释并优化强化学习代理的决策，解决车载通信系统中的关键可靠性问题。仿真结果表明，该方法提供清晰、实时的资源分配洞察，可解释精度优于纯注意力机制。同时，URLLC服务的业务质量（QoS）满意度从78.0%提升至80.13%，eMBB服务从71.44%提升至73.21%。
原文摘要 · Abstract (English)
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%.
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