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：人工智能赋能的无线接入网络（AI-RAN）已引起工业界与学术界的广泛关注。与此同时，数字孪生技术为AI/机器学习（ML）驱动的先进AI-RAN研究提供了安全的实验平台——通过在无线接入网络部署前验证在线算法，可有效降低物理实地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，专门执行链路自适应任务。我们探讨了不同的设计选择，并证明ARIADNE在频谱效率上分别超越行业标准方法与最先进技术最高可达11%和20%。最后，我们表明强化学习（RL）习得了一种与外环链路自适应（OLLA）截然不同的调制与编码策略（MCS）选择策略：其行为会根据配置呈现更保守或更激进的特性，该趋势通过基于第五代（5G）空中接口（OTA）测量数据的离线训练得到进一步验证。