面向离线多智能体网络切片的可解释人工智能引导保守式分散执行
XAI-Guided Conservative Decentralized Execution for Offline Multi-Agent Network Slicing
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中文总结 AI 辅助
该研究针对离线多智能体网络切片的资源分配问题，提出XAI引导的保守式分散执行方法，实现零资源冲突，降低信令开销与88%的有效推理延迟。
中文摘要 AI 辅助
第六代（6G）及后6G网络的最新进展，加速了对智能资源管理机制的需求，该机制需支持网络切片中共享基础设施下的异构服务。然而，网络切片中的资源分配自然形成一个资源耦合的协同优化问题，存在相互竞争的切片需求：各切片为最小化自身延迟而竞争有限资源，同时需协调以避免冲突和资源利用不足。尽管多智能体强化学习（MARL）在这类场景中表现出良好性能，但现有在线形式因依赖环境交互和智能体间通信，仍存在成本高、不安全且难以部署的问题。本研究提出可解释人工智能（XAI）引导的保守式分散执行（X-CODE），这是一种可解释的离线MARL，无需环境交互和智能体间通信即可离线运行；它利用感知可解释性的奖励塑造，在集中式训练期间修改联合离线转移的相对偏好，以优化分散式资源分配行为；部署时，智能体独立运行，无需相互交换信号。仿真结果表明，所提方法在评估的测试回合中实现零观测到的资源冲突事件，同时最小化各切片延迟；此外，与在线基线相比，在所考虑的通信延迟模型下，该框架的信令开销更低，有效推理延迟降低88%。源代码和数据集可通过此https URL获取。
英文摘要
The recent advances toward sixth-generation (6G) and beyond-6G networks have accelerated the need for intelligent resource management mechanisms capable of supporting heterogeneous services under shared infrastructures in network slicing. However, resource allocation in network slicing naturally forms a resource-coupled cooperative optimization problem with competing slice demands. Slices compete for limited resources to minimize individual latencies while coordinating to avoid conflicts and underutilization. Although multi-agent reinforcement learning (MARL) has shown promising performance in such settings, existing online formulations remain costly, unsafe, and difficult to deploy due to their reliance on environmental interactions and communication among agents. In this work, we present explainable artificial intelligence (XAI)-guided conservative decentralized execution (X-CODE). X-CODE is an explainable offline MARL that operates offline without environmental interaction, nor inter-agent communication. It exploits explainability-aware reward shaping to modify the relative preference among joint offline transitions during centralized training to improve decentralized resource-allocation behavior. In deployment, the agents operate independently without signaling exchange among the agents. Simulation results demonstrate that the proposed approach achieves zero observed resource-conflict events in the evaluated test episodes while minimizing per-slice latencies. Moreover, the proposed framework exhibits lower signaling overhead and reduces effective inference latency by 88 % under the considered communication-delay model compared to the online baselines. Source codes and datasets are available through: https://github.com/Eslam211/xcode-ran-slicing.
