6G网络突发延迟问题，用注意力多智能体强化学习快速诊断修复
Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing
- 引入六种注意力机制增强多智能体强化学习决策
- 18毫秒内解决延迟突增，恢复至0.98毫秒，可靠性达99.9999%
- 解释结果零成本生成，适合需实时可信自动化场景
第六代（6G）无线接入网（RAN）必须为异构切片严格保障服务等级协议（SLA），但传统深度强化学习（DRL）与可解释强化学习（XRL）难以应对突发延迟尖峰。本文提出注意力增强型多智能体近端策略优化（AE-MAPPO），将六种专用注意力机制融入多智能体切片控制，并以零成本、高保真方式呈现解释。该框架在O-RAN时间尺度上采用预测、响应与跨切片优化三阶段策略。在URLLC案例研究中，AE-MAPPO在18毫秒内解决延迟尖峰，将延迟恢复至0.98毫秒，可靠性达99.9999%，故障排查时间减少93%，同时保障eMBB与mMTC连续性。结果验证了其在保障SLA合规的同时具备内在可解释性，支持可信且实时的6G RAN切片自动化。
原文摘要 · Abstract (English)
Sixth-generation (6G) radio access networks (RANs) must enforce strict service-level agreements (SLAs) for heterogeneous slices, yet sudden latency spikes remain difficult to diagnose and resolve with conventional deep reinforcement learning (DRL) or explainable RL (XRL). We propose \emph{Attention-Enhanced Multi-Agent Proximal Policy Optimization (AE-MAPPO)}, which integrates six specialized attention mechanisms into multi-agent slice control and surfaces them as zero-cost, faithful explanations. The framework operates across O-RAN timescales with a three-phase strategy: predictive, reactive, and inter-slice optimization. A URLLC case study shows AE-MAPPO resolves a latency spike in $18$ms, restores latency to $0.98$ms with $99.9999\%$ reliability, and reduces troubleshooting time by $93\%$ while maintaining eMBB and mMTC continuity. These results confirm AE-MAPPO's ability to combine SLA compliance with inherent interpretability, enabling trustworthy and real-time automation for 6G RAN slicing.
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