Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing

作者：Kavan Fatehi, Mostafa Rahmani Ghourtani, Amir Sonee, Poonam Yadav, Alessandra Russo, Hamed Ahmadi, R. Calinescu · 发表于：ICC 2026 - IEEE International Conference on Communications · 年份：2026 · DOI：10.1109/icc59461.2026.11586913 · 被引用次数：1 · 研究领域：Computer Science、Engineering

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 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 18ms, restores latency to 0.98ms 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.