xDevSM: An Open-Source Framework for Portable, AI-Ready xApps Across Heterogeneous O-RAN Deployments

2026-02-03

AI Summary

This work addresses key challenges in deploying AI-driven xApps within O-RAN systems, including intricate low-level control, poor interoperability across heterogeneous RAN stacks, and the absence of developer-friendly frameworks. To overcome these limitations, the authors propose xDevSM—a novel framework that introduces, for the first time, a unified development abstraction for xApps spanning diverse RAN software stacks. By leveraging the O-RAN E2 interface, xDevSM integrates KPM-based observability with fine-grained radio resource control primitives, offering a portable and AI-ready development environment. The framework supports advanced functionalities such as slice-level PRB allocation and mobility-aware handover. Validated on a multi-vendor COTS testbed, it demonstrates seamless interoperability and successfully realizes three AI-driven closed-loop use cases: performance monitoring, slice scheduling, and intelligent handover, thereby significantly lowering the barrier to developing AI-enabled closed-loop control in O-RAN.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Openness and programmability in the O-RAN architecture enable closed-loop control of the Radio Access Network (RAN). Artificial Intelligence (AI)-driven xApps, in the near-real-time RAN Intelligent Controller (RIC), can learn from network data, anticipate future conditions, and dynamically adapt radio configurations. However, their development and adoption are hindered by the complexity of low-level RAN control and monitoring message models exposed over the O-RAN E2 interface, limited interoperability across heterogeneous RAN software stacks, and the lack of developer-friendly frameworks. In this paper, we introduce xDevSM, a framework that significantly lowers the barrier to xApp development by unifying observability and control in O-RAN deployment. By exposing a rich set of Key Performance Measurements (KPMs) and enabling fine-grained radio resource management controls, xDevSM provides the essential foundation for practical AI-driven xApps. We validate xDevSM on real-world testbeds, leveraging Commercial Off-the-Shelf (COTS) devices together with heterogeneous RAN hardware, including Universal Software Radio Peripheral (USRP)-based Software-defined Radios (SDRs) and Foxconn radio units, and show its seamless interoperability across multiple open-source RAN software stacks. Furthermore, we discuss and evaluate the capabilities of our framework through three O-RAN-based scenarios of high interest: (i) KPM-based monitoring of network performance, (ii) slice-level Physical Resource Block (PRB) allocation control across multiple User Equipments (UEs) and slices, and (iii) mobility-aware handover control, showing that xDevSM can implement intelligent closed-loop applications, laying the groundwork for learning-based optimization in heterogeneous RAN deployments. xDevSM is open source and available as foundational tool for the research community.

Problem

• O-RAN
• xApp
• interoperability
• RAN control
• developer framework

Innovation

• xDevSM
• O-RAN
• xApp
• AI-driven RAN control
• heterogeneous interoperability