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.

翻译：冲突缓解（ConMit）是开放无线接入网络（O-RAN）智能网络控制中的关键环节。本文提出名为ACCoRD的方法，通过采用强化学习算法PPO-Clip训练的具有人工神经网络（ANN）的冲突消解（CR）智能体，解决近实时RAN智能控制器中检测到的控制冲突。所实现的ANN分析网络数据及存在冲突的控制决策，以推断最优CR动作。CR智能体在每次冲突消解后从网络收集反馈，评估其效率并在批量训练过程中调整ANN权重。基于仿真数据对所提方法进行评估，同时提出一种评估CR解决方案的新方法。结果表明，所提出的基于ANN的方法在中高流量场景下通过显著减少由冲突控制决策导致的负面网络事件，优于基于规则的解决方案的效率。