一个可解释的人工智能质量感知IoE服务交付框架
An Explainable Artificial Intelligence Framework for Quality-Aware IoE Service Delivery
摘要：
第六代（6 G）无线网络的核心设想之一是积累用于万物互联（IoE）的自主控制的人工智能（AI）。特别是，必须通过分析IoE的上下文指标（如人员、数据、流程和事物）来保持IoE服务交付的质量。然而，当人工智能模型为网络服务提供商提供了一个解释和直觉的湖泊时，挑战就出现了。因此，本文提供了一个可解释的人工智能（XAI）框架，用于支持智能和解释的质量感知万物互联服务交付。首先，通过考虑网络动态和IoE的上下文度量来制定质量感知的IoE服务递送的问题，其中目标是最大化每个IoE服务用户的信道质量指数（CQI）。其次，设计了一个回归问题来解决公式化的问题，其中上下文矩阵的可解释系数由Shapley值解释来估计。第三，通过采用基于集合的回归模型来实现启用XAI的质量感知IoE服务交付算法，以确保对矩阵之间的上下文关系的解释，从而重新配置网络参数。实验结果表明，AdaBoost和Extra Trees的上行链路性能改善率分别达到42.43%和16.32%，而下行链路性能改善率分别达到28.57%和14.29%。然而，基于AdaBoost的方法不能维持IoE服务用户的CQI。因此，拟议的基于额外树的回归模型显示出显着的性能增益，以减轻准确性和可解释性之间的权衡比其他基线。
英文摘要：
One of the core envisions of the sixth-generation (6G) wireless networks is to accumulate artificial intelligence (AI) for autonomous controlling of the Internet of Everything (IoE). Particularly, the quality of IoE services delivery must be maintained by analyzing contextual metrics of IoE such as people, data, process, and things. However, the challenges incorporate when the AI model conceives a lake of interpretation and intuition to the network service provider. Therefore, this paper provides an explainable artificial intelligence (XAI) framework for quality-aware IoE service delivery that enables both intelligence and interpretation. First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user. Second, a regression problem is devised to solve the formulated problem, where explainable coefficients of the contextual matrices are estimated by Shapley value interpretation. Third, the XAI-enabled quality-aware IoE service delivery algorithm is implemented by employing ensemble-based regression models for ensuring the interpretation of contextual relationships among the matrices to reconfigure network parameters. Finally, the experiment results show that the uplink improvement rate becomes 42.43% and 16.32% for the AdaBoost and Extra Trees, respectively, while the downlink improvement rate reaches up to 28.57% and 14.29%. However, the AdaBoost-based approach cannot maintain the CQI of IoE service users. Therefore, the proposed Extra Trees-based regression model shows significant performance gain for mitigating the trade-off between accuracy and interpretability than other baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
