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

• URL: http://arxiv.org/abs/2602.11076v1
• Date: Wed, 11 Feb 2026 17:44:03 GMT
• Title: Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing
• Abstract summary: 6G radio access networks (RANs) must enforce strict service-level agreements (SLAs) for heterogeneous slices.<n>Sudden latency spikes remain difficult to diagnose and resolve with conventional deep reinforcement learning (DRL) or explainable RL (XRL)<n>We propose AE-MAPPO, which integrates six specialized attention mechanisms into multi-agent slice control and surfaces them as zero-cost, faithful explanations.
• Score: 38.896521594022616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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