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COMIX: Generalized Conflict Management in O-RAN xApps -- Architecture, Workflow, and a Power Control case

Anastasios Giannopoulos, Sotirios Spantideas, Levis George, Kalafatelis Alexandros, Panagiotis Trakadas

TL;DR

COMIX tackles the problem of conflicting objectives among xApps running on the O-RAN Near-RT RIC by introducing a generalized Conflict Mitigation Framework (CMF) paired with a Network Digital Twin (NDT) to pre-evaluate actions. It deploys two DRL-based xApps for Downlink Multi-channel Power Control—one maximizing throughput and the other optimizing energy efficiency—while resolving their conflicts through diversified resolution policies. The framework integrates policy enforcement in the Non-RT RIC, proactive conflict detection, and NDT-driven evaluation to minimize live-network disruption. Validation in a realistic multi-cell, multi-channel scenario demonstrates substantial energy savings and balanced performance, highlighting COMIX’s practical potential for multi-vendor, multi-xApp O-RAN deployments.

Abstract

Open Radio Access Network (O-RAN) is transforming the telecommunications landscape by enabling flexible, intelligent, and multi-vendor networks. Central to its architecture are xApps hosted on the Near-Real-Time RAN Intelligent Controller (Near-RT RIC), which optimize network functions in real time. However, the concurrent operation of multiple xApps with conflicting objectives can lead to suboptimal performance. This paper introduces a generalized Conflict Management scheme for Multi-Channel Power Control in O-RAN xApps (COMIX), designed to detect and resolve conflicts between xApps. To demonstrate COMIX, we focus on two Deep Reinforcement Learning (DRL)-based xApps for power control: one maximizes the data rare across UEs, and the other optimizes system-level energy efficiency. COMIX employs a standardized Conflict Mitigation Framework (CMF) for conflict detection and resolution and leverages the Network Digital Twin (NDT) to evaluate the impact of conflicting actions before applying them to the live network. We validate the framework using a realistic multi-channel power control scenario under various conflict resolution policies, demonstrating its effectiveness in balancing antagonistic objectives. Our results highlight significant network energy savings achieved through the conflict management scheme compared to baseline CMF-free methods.

COMIX: Generalized Conflict Management in O-RAN xApps -- Architecture, Workflow, and a Power Control case

TL;DR

COMIX tackles the problem of conflicting objectives among xApps running on the O-RAN Near-RT RIC by introducing a generalized Conflict Mitigation Framework (CMF) paired with a Network Digital Twin (NDT) to pre-evaluate actions. It deploys two DRL-based xApps for Downlink Multi-channel Power Control—one maximizing throughput and the other optimizing energy efficiency—while resolving their conflicts through diversified resolution policies. The framework integrates policy enforcement in the Non-RT RIC, proactive conflict detection, and NDT-driven evaluation to minimize live-network disruption. Validation in a realistic multi-cell, multi-channel scenario demonstrates substantial energy savings and balanced performance, highlighting COMIX’s practical potential for multi-vendor, multi-xApp O-RAN deployments.

Abstract

Open Radio Access Network (O-RAN) is transforming the telecommunications landscape by enabling flexible, intelligent, and multi-vendor networks. Central to its architecture are xApps hosted on the Near-Real-Time RAN Intelligent Controller (Near-RT RIC), which optimize network functions in real time. However, the concurrent operation of multiple xApps with conflicting objectives can lead to suboptimal performance. This paper introduces a generalized Conflict Management scheme for Multi-Channel Power Control in O-RAN xApps (COMIX), designed to detect and resolve conflicts between xApps. To demonstrate COMIX, we focus on two Deep Reinforcement Learning (DRL)-based xApps for power control: one maximizes the data rare across UEs, and the other optimizes system-level energy efficiency. COMIX employs a standardized Conflict Mitigation Framework (CMF) for conflict detection and resolution and leverages the Network Digital Twin (NDT) to evaluate the impact of conflicting actions before applying them to the live network. We validate the framework using a realistic multi-channel power control scenario under various conflict resolution policies, demonstrating its effectiveness in balancing antagonistic objectives. Our results highlight significant network energy savings achieved through the conflict management scheme compared to baseline CMF-free methods.
Paper Structure (23 sections, 9 equations, 8 figures, 3 tables)

This paper contains 23 sections, 9 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Open RAN Overview
  3. Conflict Management in O-RAN xApps
  4. Related Work
  5. Paper summary and contributions
  6. Architectural System Model
  7. Components and their Roles
  8. Generalized xApp Conflict Classification
  9. Design of xApps
  10. Downlink Multi-channel Power Control
  11. Optimization Problem via DRL
  12. Data Rate Maximization xApp
  13. Energy Efficiency Maximization xApp
  14. Network Digital Twin Functionality
  15. Parameterizing NDT during xApp training
  16. ...and 8 more sections

Figures (8)

  • Figure 1: Conflicts in O-RAN. (a) Conflict categories in O-RAN. (b) Architectural view of conflict categories.
  • Figure 2: General O-RAN architecture considered for COMIX.
  • Figure 3: Generalized Direct/Indirect Conflict Detector. (a) Internal Architecture; (b) Example of CPs/KPIs associated with 4 xApps; (c) CP Clusters per KPI; (d) Conflict Graph.
  • Figure 4: DRL cycles between xApps and O-RAN environment.
  • Figure 5: Learning curves of DRM (a-c) and EE (d-f) xApps for different learning rates (column 1), discount factors (column 2), and power steps (column 3).
  • ...and 3 more figures