o-ran integration

To address the high entry barrier, fragmented and outdated documentation, and lack of practical validation environments for xApp development in the O-RAN ecosystem, this paper proposes the first comprehensive, end-to-end xApp development paradigm. The paradigm systematically covers architectural analysis, design and configuration, lifecycle management, E2 interface invocation, RAN control, and debugging. Grounded in O-RAN Alliance specifications, it integrates the Near-Real-Time RIC architecture, xApp SDK, containerized deployment, and closed-loop real-time policy control. The resulting workflow is fully reproducible and validated. This approach significantly reduces learning and trial-and-error costs for third-party developers, accelerates algorithm prototyping and deployment, and bridges the critical gap between academia and industry by providing the first practical, implementation-oriented xApp development guide. It thereby advances the adoption of intelligent, programmable O-RAN network applications.

This study addresses the high risks and costs associated with validating O-RAN closed-loop control algorithms in real-world environments by proposing a modular, cloud-native digital twin framework. By integrating the Juniper RIC platform with Keysight RICtest emulation technology, the framework establishes an isolated and reproducible experimental environment that automates the entire workflow, including xApp/rApp onboarding, scenario configuration, Key Performance Measurement (KPM) monitoring, and dataset generation. Experimental results successfully validate the effectiveness of closed-loop strategies such as cell switch-off control. Ultimately, this work provides a secure, efficient, and scalable testing paradigm for O-RAN intelligent control, significantly reducing the barriers to deploying and evaluating RAN automation policies prior to live network implementation.

This paper addresses the objective conflicts arising from multi-AI-driven xApp/rApp co-control in O-RAN—e.g., conflicting goals of throughput maximization versus energy minimization—by proposing the first end-to-end framework for conflict detection and severity quantification tailored to O-RAN. Methodologically, it integrates sandbox-based pre-deployment rehearsal, hierarchical graph modeling, and statistical inference to enable predictive, assessable, and mitigatable conflict analysis prior to deployment. Its key contributions include a lightweight conflict assessment model and empirical validation via integration with the Colosseum and OpenRAN Gym simulation platforms. Experimental results demonstrate that the framework proactively identifies latent conflicts, averting an average 16% throughput degradation and up to 30% overall system performance deterioration. It significantly enhances the accuracy of xApp selection and scheduling decisions, thereby establishing a robust foundation for conflict-aware intelligent closed-loop control in O-RAN.

This paper addresses direct, indirect, and implicit conflicts among xApps in O-RAN architectures arising from structural decoupling. It proposes the first xApp-level three-category conflict classification framework; develops a conflict graph-based KPI impact modeling method that integrates SLA/QoS thresholds for precise conflict detection; and designs a cooperative mitigation mechanism tailored to mobility robustness optimization (MRO) and energy-saving (ES) scenarios. Evaluated in a simulated environment co-deploying MRO and ES xApps, the approach significantly reduces KPI anomaly rates while enhancing both system stability and energy-efficiency synergy. Key contributions include: (i) the first formal, multidimensional definition of xApp-level conflicts; (ii) establishment of a conflict–KPI mapping model grounded in operational semantics; and (iii) realization of policy-driven, cross-xApp collaborative conflict mitigation—enabling adaptive, SLA-aware resource orchestration in disaggregated RAN environments.

To address control decision conflicts among multi-vendor xApps running on the Near-Real-Time RAN Intelligent Controller (Near-RT-RIC) in Open RAN, this paper proposes a QoS-aware conflict resolution method that maximizes the number of coexisting xApps while satisfying end-to-end QoS constraints. The approach explicitly models QoS requirements as optimization objectives, enabling joint optimization of resource allocation and service assurance, and supports adaptive solving for both priority- and non-priority-aware scenarios. We formulate the problem as an Integer Linear Program (ILP), incorporating multi-objective constrained optimization, formal QoS-to-resource mapping, and decoupling of conflict-related parameters. Experimental results demonstrate significant improvements: the number of xApps meeting their QoS targets increases by 37% under priority-aware scheduling and by 29% in non-priority-aware settings, while maintaining system-wide control consistency.

This work addresses the challenges of sparse Key Performance Metric (KPM) data caused by interface latency in O-RAN and the risk of service disruption from in-situ AI testing. To overcome these issues, the authors propose OpenTwin, a digital twin framework built upon an open-source O-RAN simulator. OpenTwin employs a two-stage machine learning approach: it first uses XGBoost to accurately infer missing KPMs and then applies a time-aware Recursive Least Squares (RLS) tuner for closed-loop control. A novel bias-aware synchronization mechanism dynamically models network behavior and automatically re-synchronizes the twin with the physical network to maintain fidelity. Experimental results on the ns-O-RAN-flexRIC platform demonstrate that OpenTwin achieves 96% accuracy in KPM reconstruction while significantly reducing energy consumption—all without introducing any interference to live network operations.

This work addresses the high energy consumption of mobile networks by proposing BeGREEN, an AI-driven intelligent plane within the O-RAN architecture to enable autonomous, energy-efficient radio access network management. By integrating an AI engine with rApps/xApps coordination mechanisms into the O-RAN intelligent plane for the first time, the approach establishes an end-to-end energy efficiency optimization loop that dynamically controls the operational states of simulated cells. Leveraging AI/ML algorithms, the O-RAN intelligent plane framework, and cell state management techniques, the proposed method significantly reduces base station energy consumption in simulation environments, thereby demonstrating the feasibility and effectiveness of AI-driven energy optimization in O-RAN networks.

This work addresses the operational complexity of 5G/6G O-RAN networks arising from their decoupled architecture and fine-grained control, which hinder the correlation of heterogeneous events and safe generation of configuration actions. The authors propose Net Analyzer rApp, the first framework to integrate a large language model (LLM) as a reasoning collaborator within the O-RAN non-real-time RIC, establishing an event-driven batch inference pipeline for mobility event parsing, anomaly validation, and configuration auditing. By incorporating tool gating, log-directed verification, and human-in-the-loop approval mechanisms, the system strictly decouples reasoning from execution, ensuring auditability and operational safety. Evaluated on a real-world O-RAN testbed under a ping-pong handover scenario, the approach successfully transforms raw telemetry into structured explanations and controlled remediation recommendations, demonstrating both efficacy and security.

本文针对O-RAN架构下RIC生态系统面临的运行时安全风险，通过引入rApp/xApp认证机制来验证应用程序的完整性，利用现有技术并通过特定模块和接口实现。

This work addresses the challenges of supporting low-latency, high-performance, and strongly isolated real-time distributed applications (dApps) in radio access networks, where existing approaches often suffer from high network complexity or insufficient security isolation. To overcome these limitations, the paper introduces WebAssembly (Wasm) into the O-RAN architecture for the first time, natively embedding a lightweight dApp runtime on the RAN side. This design enables sandboxed isolation, rapid startup, and deterministic execution without requiring additional edge resources or introducing extra security risks. Experimental results demonstrate that the proposed approach achieves strong isolation while delivering predictable low-latency performance, effectively enabling real-time closed-loop control within the O-RAN framework.