ACCoRD: Actor-Critic Conflict Resolution with Deep learning for O-RAN xApps
For O-RAN network operators, this provides an adaptive conflict resolution method that outperforms static rules, though it is an incremental improvement over existing RL-based approaches.
ACCoRD uses a reinforcement learning-trained neural network to resolve control conflicts in O-RAN, reducing negative network events by 30-50% compared to rule-based methods in medium and high traffic scenarios.
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.