← 研踪 PaperTrailarXiv:2611.12638 · v1
FAROS: Forecast-Aware Risk-Informed Online Reinforcement Learning for Proactive O-RAN Slicing
首次提交 2026-11-10 00:15
版本更新 2026-11-10 00:15
雷达观测 2026-11-10 15:22
摘要
Proactive Open Radio Access Network (O-RAN) slicing requires decisions before an approaching quality-of-service (QoS) loss becomes difficult to reverse. We present FAROS, a Forecast-Aware Risk-Informed O-RAN Slicing framework that combines an eight-step QoS forecast, training-only PCMCI+-supported temporal risk, Multi-Agent Proximal Policy Optimization (MAPPO) under centralized training and decentralized execution (CTDE), symbolic feasibility constraints, and reliability-aware Top-K arbitration. MAPPO supplies the actor proposal, while A5v2 checks admissibility and one-step utility before execution. Evaluation uses ten validation-selected frozen checkpoints, 500 matched start indices per seed under the risky-start protocol, and 150,000 agent-level decisions. FAROS obtains mean reward 0.2778 and ten-step recovery 90.56% across ten seeds. A fixed model-matched diagnostic rule reaches reward 0.3015 and recovery 97.57% under the same transition-reward contract, so we make no superiority claim over direct threshold logic. A same-actor M4 shield control also reaches zero risky-state prohibited actions. In the M5 trace, A5v2 changes 22.61% of actor proposals and reduces the predicate-defined prohibited-action rate from 16.79% at proposal time to zero at execution; this property is enforced by the arbiter rather than learned by MAPPO. Independent ns-3 experiments test the six-action interface at packet level and remain separate from end-to-end FAROS claims.
作者备注：16 pages, 9 figures. Submitted to IEEE Transactions on Network and Service Management (TNSM)
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v1 · 2026-11-10 00:15 · 最新观测版本
FAROS: Forecast-Aware Risk-Informed Online Reinforcement Learning for Proactive O-RAN Slicing
Proactive Open Radio Access Network (O-RAN) slicing requires decisions before an approaching quality-of-service (QoS) loss becomes difficult to reverse. We present FAROS, a Forecast-Aware Risk-Informed O-RAN Slicing framework that combines an eight-step QoS forecast, training-only PCMCI+-supported temporal risk, Multi-Agent Proximal Policy Optimization (MAPPO) under centralized training and decentralized execution (CTDE), symbolic feasibility constraints, and reliability-aware Top-K arbitration. MAPPO supplies the actor proposal, while A5v2 checks admissibility and one-step utility before execution. Evaluation uses ten validation-selected frozen checkpoints, 500 matched start indices per seed under the risky-start protocol, and 150,000 agent-level decisions. FAROS obtains mean reward 0.2778 and ten-step recovery 90.56% across ten seeds. A fixed model-matched diagnostic rule reaches reward 0.3015 and recovery 97.57% under the same transition-reward contract, so we make no superiority claim over direct threshold logic. A same-actor M4 shield control also reaches zero risky-state prohibited actions. In the M5 trace, A5v2 changes 22.61% of actor proposals and reduces the predicate-defined prohibited-action rate from 16.79% at proposal time to zero at execution; this property is enforced by the arbiter rather than learned by MAPPO. Independent ns-3 experiments test the six-action interface at packet level and remain separate from end-to-end FAROS claims.
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