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.

翻译：人工智能（AI）驱动的无线接入网（RAN）已引起工业界和学术界的广泛关注。同时，数字孪生技术为先进的AI-RAN研究提供了安全的实验平台，可在AI/机器学习（ML）解决方案部署至实际RAN前进行在线算法测试，从而降低物理现场测试的成本与安全风险。本文提出ARIADNE——一种与SIONNA无缝集成的在线强化学习（RL）模块，专用于实现链路自适应。我们探讨了多种设计方案，并证明ARIADNE相比行业标准方法和现有最优方法，可分别实现最高11%和20%的频谱效率提升。最后，研究表明RL学习到的调制与编码方案（MCS）选择策略与传统的闭环链路自适应（OLLA）存在差异，其行为根据配置不同表现出更保守或更激进的特性，这一趋势通过基于第五代（5G）空中（OTA）测量数据的离线训练得到进一步验证。