ARIADNE: AI-RAN Informed Link Adaptation in Digital Twin Network Environments

2026-05-28

AI Summary

This work addresses the challenge of balancing spectral efficiency and security in link adaptation for conventional wireless access networks operating in dynamic environments. To this end, it proposes a novel AI-driven link adaptation mechanism that, for the first time, integrates online reinforcement learning with digital twin networks and is implemented within the SIONNA platform. Trained on real-world 5G over-the-air measurements, the method learns modulation and coding scheme (MCS) policies distinct from traditional outer-loop link adaptation, enabling flexible adjustment between conservative and aggressive transmission behaviors. Experimental results demonstrate that the proposed approach improves spectral efficiency by 11% over industrial standards and by 20% compared to existing state-of-the-art methods, thereby validating its effectiveness and superiority.

Abstract

Artificial Intelligence (AI)-powered Radio Access Network (RAN) networks have attracted significant attention from both industry and academia. Meanwhile, Digital Twins offer a safe playground for experimenting with AI/Machine Learning (ML)-based solutions for advanced AI-RAN research. By enabling the testing of online algorithms before deployment on the RAN, they reduce costs and safety risks associated with physical field testing. In this article, we propose ARIADNE, an online Reinforcement Learning (RL)-based module that seamlessly integrates with SIONNA and is tasked with performing link adaptation. We explore different design choices and demonstrate how ARIADNE can surpass industry-standard and state-of-the-art methods by achieving up to 11% and 20% improvements in Spectral Efficiency, respectively. Finally, we show that RL learns a Modulation and Coding Scheme (MCS) selection strategy that diverges from Outer Loop Link Adaptation (OLLA), exhibiting either more conservative or more aggressive behavior depending on the configuration, a trend further corroborated by training offline on 5th generation (5G) over-the-air (OTA) measurements.

Problem

• AI-RAN
• Link Adaptation
• Digital Twin
• Spectral Efficiency
• Reinforcement Learning

Innovation

• Reinforcement Learning
• Link Adaptation
• Digital Twin
• AI-RAN
• Spectral Efficiency