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

2025-01-24

AI Summary

To address network performance degradation caused by conflicting optimization objectives among multiple xApps in O-RAN, this paper proposes COMIX—a generic conflict management framework. COMIX introduces, for the first time, a standardized Conflict Mitigation Framework (CMF) synergized with a Network Digital Twin (NDT), enabling proactive conflict detection, quantitative impact assessment, and policy-driven resolution—thereby transcending conventional single-objective optimization paradigms. Technically, it integrates Deep Reinforcement Learning (DRL), standardized E2/xApp interfaces, and a multi-strategy conflict decision engine. In real-world multi-channel power control experiments, COMIX achieves a 37% improvement in system energy efficiency over a no-conflict-management baseline, while maintaining user equipment (UE) throughput stability. These results empirically validate the effectiveness and practicality of cross-objective collaborative optimization in O-RAN environments.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• O-RAN architecture
• xApps conflict
• network performance degradation

Innovation

• COMIX
• Conflict Management Framework (CMF)
• Deep Reinforcement Learning