Ai enables smarter open radio access network control and optimization
AI-Native Open RAN: A Roadmap from xApps and rApps to Autonomous Network Agents
Networking and Internet Architecture
Summary
Managing wireless networks is complicated, and new open systems called O-RAN aim to make this easier by using artificial intelligence (AI). The authors review how AI is being integrated into these networks to improve things like traffic handling, resource allocation, and reducing interference. They explain the technical setup of O-RAN systems and show how different AI methods, including machine learning and reinforcement learning, help networks work better. However, existing AI solutions often only solve specific problems and don’t easily adapt to changing conditions or different network environments.
What this means in practice
- •For network operators: Improve wireless network performance by selecting AI techniques suited for near-real-time and non-real-time control within open RAN environments.
- •For wireless infrastructure engineers: Design and deploy AI-driven components that adapt to diverse network conditions using a taxonomy of machine learning and reinforcement learning methods in O-RAN.
A survey. It maps existing work.
Authors
Ryan Barker, Alireza Ebrahimi Dorcheh, Tolunay Seyfi, Mohammad Raihan Uddin, Alireza Mohammadhosseini, Julia Boone, Stephen Streit, Drew Schlesener, Fatemeh Afghah
Abstract
Open Radio Access Networks (O-RAN) have emerged as a transformative paradigm for future wireless systems by introducing openness, virtualization, disaggregation, and programmable intelligence through the RAN Intelligent Controller (RIC). The availability of standardized interfaces and near-real-time control loops has created unprecedented opportunities for integrating artificial intelligence (AI) into radio access network management and optimization. Over the past several years, a broad range of AI techniques have been proposed to address key O-RAN challenges such as radio resource management, network slicing, traffic prediction, mobility management, interference mitigation, and spectrum sharing. Despite significant progress, existing solutions often remain task-specific, require extensive retraining, and exhibit limited generalization across deployment environments and network conditions. This paper presents a comprehensive review of AI-enabled O-RAN systems and provides a unifying perspective on the evolution of intelligence in wireless networks. We first examine the O-RAN architecture and the role of intelligence within near-real-time and non-real-time RIC frameworks. We then develop a taxonomy of AI approaches for O-RAN, covering machine learning, deep reinforcement learning (DRL), digital-twin-assisted optimization, and emerging foundation-model-based architectures.