ACCoRD: Actor-Critic Conflict Resolution with Deep learning for O-RAN xApps

2026-05-22 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

The paper "ACCoRD: Actor-Critic Conflict Resolution with Deep learning for O-RAN xApps" introduces ACCoRD, a novel method designed to resolve control conflicts within Near-Real Time RAN Intelligent Controllers (RICs) in Open Radio Access Networks (O-RAN). ACCoRD employs a Conflict Resolution (CR) Agent equipped with an Artificial Neural Network (ANN), which is trained using the PPO-Clip reinforcement learning algorithm. This ANN processes network data and conflicting control decisions to determine optimal conflict resolution actions. The CR Agent continuously gathers feedback from the network post-resolution, using this data to adjust the ANN's weights through batch training. Evaluated using simulation data, the proposed approach, which also includes a new methodology for CR solution assessment, demonstrates improved efficiency compared to traditional rule-based methods. It significantly reduces negative network events caused by conflicting decisions, particularly in medium and high traffic scenarios.

Key takeaway

For Machine Learning Engineers designing intelligent controllers for Open Radio Access Networks, you should consider integrating reinforcement learning-trained Artificial Neural Networks for conflict resolution. ACCoRD demonstrates that an ANN-based approach, specifically using PPO-Clip, significantly reduces negative network events compared to traditional rule-based systems in medium and high traffic. This suggests a shift towards adaptive, learning-based conflict mitigation can improve network stability and performance.

Key insights

ACCoRD uses a PPO-Clip trained ANN to resolve O-RAN control conflicts, outperforming rule-based methods by reducing negative network events.

Principles

• Reinforcement learning enhances O-RAN conflict resolution.
• Feedback loops are crucial for adaptive CR agents.
• ANN-based CR improves efficiency over rule-based systems.

Method

An ANN-based CR Agent, trained with PPO-Clip, analyzes network data and conflicting decisions to infer optimal actions. It then gathers network feedback to adjust ANN weights via batch training.

In practice

• Apply PPO-Clip for dynamic network control.
• Implement ANNs for real-time conflict mitigation.
• Use simulation for CR solution evaluation.

Topics

• O-RAN
• Conflict Resolution
• Reinforcement Learning
• PPO-Clip
• Artificial Neural Networks
• Multiagent Systems

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.