Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we develop a RIC platform called RICworld, consisting of (i) EdgeRIC, which is colocated, but decoupled from the RAN stack, and can access RAN and application-level information to execute AI-optimized and other policies in realtime (sub-millisecond) and (ii) DigitalTwin, a full-stack, trace-driven emulator for training AI-based policies offline. We demonstrate that realtime EdgeRIC operates as if embedded within the RAN stack and significantly outperforms a cloud-based near-realtime RIC (> 15 ms latency) in terms of attained throughput. We train AI-based polices on DigitalTwin, execute them on EdgeRIC, and show that these policies are robust to channel dynamics, and outperform queueing-model based policies by 5% to 25% on throughput and application-level benchmarks in a variety of mobile environments.

翻译：摘要：无线接入网络（RAN）越来越软件化，可以通过数据收集和控制接口进行访问。 RAN 智能控制（RIC）是管理这些接口的方法。在本文中，我们开发了一个名为 RICworld 的 RIC 平台，其中包括（i）EdgeRIC，它与 RAN 堆栈相邻，但与之分离，并可以访问 RAN 和应用程序级别的信息，以实时（亚毫秒级别）执行 AI 优化和其他策略，（ii）DigitalTwin，一个完整的，基于轨迹驱动的仿真程序，用于离线训练基于 AI 的策略。 我们展示了实时 EdgeRIC 的操作方式，就像嵌入在 RAN 堆栈中一样，并且在实现的吞吐量方面显著优于基于云的准实时 RIC（> 15 ms 延迟）。我们在 DigitalTwin 上训练基于 AI 的策略，在 EdgeRIC 上执行它们，并展示了这些策略针对信道动态具有鲁棒性，并且在各种移动环境中比基于队列模型的策略在吞吐量和应用程序级别基准上优于 5％ 至 25％。