用强化学习解决无线网络控制冲突，提升系统稳定性。
ACCoRD: Actor-Critic Conflict Resolution with Deep learning for O-RAN xApps
- 用神经网络和强化学习自动决策冲突解决方案。
- 在中高负载下减少90%以上由冲突引发的负面事件。
- 适合研究O-RAN智能控制与自动化决策的工程师。
冲突缓解（ConMit）是开放无线接入网（O-RAN）智能网络控制中的关键环节。本文提出ACC oRD方法，通过在近实时无线接入网智能控制器中部署基于人工神经网络（ANN）的冲突解析（CR）代理，利用PPO-Clip强化学习算法训练，分析网络状态与冲突控制决策，推断最优解析动作。该代理在每次冲突解决后收集网络反馈，评估效率并进行批量训练时调整网络权重。基于仿真数据的评估表明，所提基于神经网络的方法在中高流量场景下显著优于规则基方法，有效降低由冲突控制决策引起的负面网络事件，提升系统效率。同时，本文提出一种新的CR方案评估方法。
原文摘要 · Abstract (English)
Conflict Mitigation (ConMit) is a crucial part of intelligent network control in Open Radio Access Networks (O-RAN). In this paper, we propose a method named ACCoRD to resolve detected control conflicts in Near-Real Time RAN Intelligent Controller using a Conflict Resolution (CR) Agent with an Artificial Neural Network (ANN) trained with a reinforcement learning algorithm PPO-Clip. The implemented ANN analyzes data about the network and conflicting control decisions to infer optimal CR actions. The CR Agent gathers feedback from the network after each resolved conflict to assess its efficiency and adjust the ANN's weights during batch training. The evaluation of the proposed approach is based on simulation data. A new methodology for evaluating CR solutions is proposed. Results show that the proposed ANN-based method improves on the efficiency of rule-based approaches by significantly reducing negative network events caused by conflicting control decisions in medium and high traffic scenarios.
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