  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.
