Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing
This addresses the challenge of ensuring strict service-level agreements for heterogeneous slices in 6G networks, offering a solution for network operators to achieve real-time automation with interpretability.
The paper tackled the problem of diagnosing and resolving sudden latency spikes in 6G radio access network slicing, proposing an attention-based multi-agent PPO method that resolved a latency spike in 18ms, restored latency to 0.98ms with 99.9999% reliability, and reduced troubleshooting time by 93%.
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.