发表机构
Aalborg University（奥尔堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对6G in-X子网动态干扰与异构需求，提出融合多智能体强化学习、联邦学习与可解释AI的框架，实现隐私保护下的联合资源分配与调度，仿真验证性能与透明度显著提升。
AI 中文摘要
第六代（6G）无线系统被设想为网络的网络，集成各种in-X子网，以提供本地化、高性能的连接。在工业机器人和车辆等密集部署场景中，由于动态干扰和严格的性能要求，确保可靠通信具有挑战性。传统的无线资源管理（RRM）方法存在局限性，促使需要基于AI的解决方案。在本文中，我们通过提出一种结合多智能体强化学习（MARL）、联邦学习（FL）和可解释人工智能（XAI）的新框架，解决了支持具有异构特性应用的6G in-X子网中的动态资源分配和调度挑战。我们的解决方案旨在提高资源管理和子网内调度的可靠性、鲁棒性和透明度，同时确保多个共存子网之间的数据隐私和公平性。与现有工作不同，我们的方法考虑了每个子网具有多个设备的现实场景，从而提供了更全面和可扩展的解决方案。所提出的可解释强化学习框架使智能体能够协作优化信道分配和调度，而无需共享原始数据，从而保护每个参与子网的隐私。基于3GPP场景的广泛仿真证明了我们方法的有效性，在性能和透明度方面相较于现有解决方案显示出显著改进。
英文摘要
Sixth-generation (6G) wireless systems are envisioned as networks of networks, integrating diverse in-X subnetworks that provide localized, high-performance connectivity. Ensuring reliable communication in dense deployments, such as industrial robots and vehicles, is challenging due to dynamic interference and strict performance requirements. Traditional radio resource management (RRM) methods have limitations, prompting the need for AI-based solutions. In this paper, we address the challenges of dynamic resource allocation and scheduling in 6G in-X subnetworks supporting applications with heterogeneous characteristics by proposing a novel framework that combines Multi-Agent Reinforcement Learning (MARL), Federated Learning (FL), and Explainable AI (XAI). Our solution is designed to improve the reliability, robustness, and transparency of resource management and intra-subnetwork scheduling while ensuring data privacy and fairness across multiple co-existing subnetworks. Unlike existing works, our approach considers a realistic scenario with multiple devices per subnetwork, thereby offering a more comprehensive and scalable solution. The proposed explainable RL framework enables agents to collaboratively optimize channel allocation and scheduling without the need to share raw data, preserving the privacy of each participating subnetwork. Extensive simulations based on 3GPP scenarios demonstrate the effectiveness of our approach, showing significant improvements in performance, and transparency over existing solutions.
CommentsAccepted in IEEE VTC Fall 2026
