AI-Enhanced Service Management and Orchestration for rApp and xApp Lifecycle Automation in Open RAN

Abstract

The development of Open Radio Access Network (Open RAN) systems has brought better operational flexibility and the ability to work with different vendors and the ability to control network operations through its separate network elements. The Service Management and Orchestration (SMO) layer serves as the main component that enables the Non-Real-Time RAN Intelligent Controller (Non-RT RIC) to coordinate with the Near-Real-Time RIC (Near-RT RIC). The operation of Open RAN systems becomes harder because network applications and xApps need to be controlled throughout their entire lifecycle while multiple network vendors provide different services and network usage demands require organizations to meet specific Quality of Service requirements.The paper presents an AI-based SMO framework which uses telemetry data to manage rApp and xApp lifecycles through automated processes. The framework uses machine learning models for analyzing network telemetry data which helps identify performance problems and forecast resource shortages while providing recommendations for effective remediation procedures. The system uses three technologies which include anomaly detection and predictive analytics and policy-based orchestrationto automatically manage the entire lifecycle of rApps and xApps.Through its closed-loop automation mechanisms the framework creates a system which maintains ongoing feedback between its monitoring systems and its orchestration engines.The experimental results show that the system achieves better fault detection accuracy while decreasing mean time to recovery (MTTR) and improving resource efficiency. The proposed AI-based coordination system improves synchronization between near-real-time control functions and non-real-time control functions which leads to better network performance and higher service reliability.The results show that AI-enhanced SMO technology can decrease operational challenges while enhancing system flexibility and capacity to handle dense Open RAN networks. The study develops a functional guide that shows how artificial intelligence should be integrated into Open RAN systems to create fully autonomous self-optimizing intelligent RAN networks of the future.