RIC: A RAN Intelligent Controller Platform for AI-Enabled Cellular Networks

Abstract

With the emergence of 5G, network densification, and richer and more demanding applications, the radio access network (RAN)—a key component of the cellular network infrastructure—will become increasingly complex. To tackle this complexity, it is critical for the RAN to be able to automate the process of deploying, optimizing, and operating while leveraging novel data-driven technologies to ultimately improve the end-user quality of experience. In this article, we disaggregate the traditional monolithic control plane (CP) RAN architecture and introduce a RAN Intelligent Controller (RIC) platform decoupling the control and data planes of the RAN driving an intelligent and continuously evolving radio network by fostering network openness and empowering network intelligence with AI-enabled applications. We provide functional and software architectures of the RIC and discuss its design challenges. We elaborate how the RIC can enable near-real-time network optimization in 5G for the dual-connectivity use case using machine learning control loops. Finally, we provide preliminary results to evaluate the performance of our open-source RIC platform.