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

• URL: http://arxiv.org/abs/2605.22306v1
• Date: Thu, 21 May 2026 10:54:57 GMT
• Title: ACCoRD: Actor-Critic Conflict Resolution with Deep learning for O-RAN xApps
• Abstract summary: Conflict Mitigation (ConMit) is a crucial part of intelligent network control in Open Radio Access Networks (O-RAN)<n>In this paper, we propose a method named ACCoRD to resolve detected control conflicts in Near-Real Time RAN Intelligent Controller.<n>The implemented ANN analyzes data about the network and conflicting control decisions to infer optimal CR actions.
• Score: 4.096540146408279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks [71.01350912967922]
  In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.<n>This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services.<n>We develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-08-02T10:05:50Z)
• Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks [6.373998211961586]
  Reinforcement learning (RL) has been increasingly applied to network control problems, such as load balancing.<n>Existing RL approaches often suffer from lack of interpretability and difficulty in extracting controller equations.<n>We propose the use of Kolmogorov-Arnold Networks (KAN) for interpretable RL in network control.
  arXiv  Detail & Related papers  (2025-05-20T14:56:31Z)
• Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach [51.63921041249406]
  Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. A novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication.
  arXiv  Detail & Related papers  (2024-06-19T07:17:04Z)
• Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning [5.1435595246496595]
  This paper introduces a novel generative AI (GAI)-driven, unified semi-supervised learning architecture for optimizing resource allocation and network slicing in O-RAN.<n>Termed Generative Semi-Supervised VAE-Contrastive Learning, our approach maximizes the weighted user equipment (UE) throughput and allocates physical resource blocks (PRBs) to enhance the quality of service for eMBB and URLLC services.
  arXiv  Detail & Related papers  (2024-01-16T22:23:27Z)
• UAV Path Planning Employing MPC- Reinforcement Learning Method for search and rescue mission [0.0]
  We tackle the problem of Unmanned Aerial (UA V) path planning in complex and uncertain environments. We design a Model Predictive Control (MPC) based on a Long-Short-Term Memory (LSTM) network integrated into the Deep Deterministic Policy Gradient algorithm.
  arXiv  Detail & Related papers  (2023-02-21T13:39:40Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• Decentralized Channel Management in WLANs with Graph Neural Networks [17.464353263281907]
  Wireless local area networks (WLANs) manage multiple access points (APs) and assign radio frequency to APs for satisfying traffic demands. This paper puts forth a learning-based solution that can be implemented in a decentralized manner.
  arXiv  Detail & Related papers  (2022-10-30T21:14:45Z)
• A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks [58.720142291102135]
  We consider a radio resource management (RRM) problem in a multi-user wireless network. The goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We propose a state-augmented parameterization for the RRM policy, where alongside the instantaneous network states, the RRM policy takes as input the set of dual variables corresponding to the constraints.
  arXiv  Detail & Related papers  (2022-10-28T21:24:13Z)
• State-Augmented Learnable Algorithms for Resource Management in Wireless Networks [124.89036526192268]
  We propose a state-augmented algorithm for solving resource management problems in wireless networks. We show that the proposed algorithm leads to feasible and near-optimal RRM decisions.
  arXiv  Detail & Related papers  (2022-07-05T18:02:54Z)
• Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks [82.02891936174221]
  Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach. In this paper, a novel semantic-aware CDRL method is proposed to enable a group of untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.
  arXiv  Detail & Related papers  (2021-11-23T18:24:47Z)
• Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks [8.472603460083375]
  We propose consensus multi-agent deep reinforcement learning algorithm to solve the VVC problem. The proposed algorithm allows individual agents to learn a group control policy using local rewards. Numerical studies on IEEE distribution test feeders show that our proposed algorithm matches the performance of single-agent reinforcement learning benchmark.
  arXiv  Detail & Related papers  (2020-07-06T18:21:47Z)
• Multi-agent Reinforcement Learning for Networked System Control [6.89105475513757]
  This paper considers multi-agent reinforcement learning (MARL) in networked system control. We propose a new different communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL. NeurComm outperforms existing communication protocols in both learning efficiency and control performance.
  arXiv  Detail & Related papers  (2020-04-03T02:21:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.