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，并通过数据采集与控制接口实现可访问性。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，其中包括：（i）EdgeRIC，它共址部署但解耦于RAN协议栈，可实时（亚毫秒级）访问RAN及应用层信息以执行AI优化策略及其他策略；（ii）DigitalTwin，一个全栈式、基于轨迹驱动的仿真器，用于离线训练基于AI的策略。我们证明，实时EdgeRIC如同嵌入RAN协议栈内部运行，在吞吐量方面显著优于基于云端的近实时RIC（延迟>15毫秒）。我们利用DigitalTwin训练基于AI的策略，并在EdgeRIC上执行这些策略，结果表明这些策略对信道动态变化具有鲁棒性，且在多种移动环境中，其吞吐量及应用层基准指标比基于排队模型的策略提升5%至25%。