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.
@article{arxiv.2304.11199,
title = {EdgeRIC: Empowering Realtime Intelligent Optimization and Control in NextG Networks},
author = {Woo-Hyun Ko and Ushasi Ghosh and Ujwal Dinesha and Raini Wu and Srinivas Shakkottai and Dinesh Bharadia},
journal= {arXiv preprint arXiv:2304.11199},
year = {2023}
}