ICRAN: Intelligent Control for Self-Driving RAN Based on Deep Reinforcement Learning

Abstract

Mobile networks are increasingly expected to support use cases with diverse performance expectations at a very high level of reliability. These expectations imply the need for approaches that timely detect and correct performance problems. However, current approaches often focus on optimizing a single performance metric. Here, we aim to address this gap by proposing a novel control framework that maximizes radio resources utilization and minimizes performance degradation in the most challenging part of cellular architecture that is the radio access network (RAN). We devise a method called Intelligent Control for Self-driving RAN (ICRAN) which involves two deep reinforcement learning based approaches that control the RAN in a centralized and a distributed way, respectively. ICRAN defines a dual-objective optimization goals that are achieved through a set of diverse control actions. Using extensive discrete event simulations, we confirm that ICRAN succeeds in achieving its design goals, showing a greater edge over competing approaches. We believe that ICRAN is implementable and can serve as an important point on the way to realizing self-driving mobile networks.