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：无线接入网络（RAN）正日益软件化，并通过数据收集与控制接口变得可访问。RAN智能控制（RIC）是一种在不同时间尺度上管理这些接口的方法。本文开发了一个名为RICworld的RIC平台，包含：(i) EdgeRIC，该组件与RAN栈共置但解耦，能够访问RAN及应用层面的信息，以毫秒级实时执行AI优化策略及其他策略；(ii) DigitalTwin，一个全栈、基于追踪驱动的仿真器，用于离线训练基于AI的策略。我们证明，实时EdgeRIC如同嵌入RAN栈内运行，且在吞吐量方面显著优于基于云的近实时RIC（延迟>15毫秒）。我们在DigitalTwin上训练基于AI的策略，并在EdgeRIC上执行，结果表明这些策略对信道动态具有鲁棒性，并在多种移动环境中，吞吐量和应用级基准测试上比基于排队模型的策略提升5%至25%。