用可解释AI精简DRL模型特征，提升6G车联网资源分配效率
Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation
- 基于SHAP的可解释性分析，自动排序状态特征重要性
- 删减28%不重要特征后仍保持97%原始性能，训练时间减少11%
- 适合需要高效、透明的智能网络资源管理场景
人工智能有望显著提升第六代（6G）网络中的无线资源管理能力。然而，复杂深度学习模型缺乏可解释性，制约了实际应用。本文提出一种无需依赖具体模型的可解释人工智能（XAI）框架，用于特征选择与模型复杂度降低。应用于多智能体深度强化学习（MADRL）场景，解决蜂窝车联网（V2X）通信中的子带分配与功率分配联合问题。提出两阶段系统化可解释框架：第一阶段使用基于SHAP的贡献度评分，对训练好的模型生成状态特征重要性排序；第二阶段依据该排序，从模型输入中移除最不重要的特征，简化状态空间。仿真结果表明，在包含八个车载终端对的网络中，该方法在保留原MADRL总吞吐量97%的同时，将最优状态特征数量减少28%，平均训练时间缩短11%，可训练参数减少46%。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. However, the lack of explainability in complex deep learning (DL) models poses a challenge for practical implementation. This paper proposes a novel explainable AI (XAI)- based framework for feature selection and model complexity reduction in a model-agnostic manner. Applied to a multi-agent deep reinforcement learning (MADRL) setting, our approach addresses the joint sub-band assignment and power allocation problem in cellular vehicle-to-everything (V2X) communications. We propose a novel two-stage systematic explainability framework leveraging feature relevance-oriented XAI to simplify the DRL agents. While the former stage generates a state feature importance ranking of the trained models using Shapley additive explanations (SHAP)-based importance scores, the latter stage exploits these importance-based rankings to simplify the state space of the agents by removing the least important features from the model input. Simulation results demonstrate that the XAI-assisted methodology achieves 97% of the original MADRL sum-rate performance while reducing optimal state features by 28%, average training time by 11%, and trainable weight parameters by 46% in a network with eight vehicular pairs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。