RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN

Abstract

Radio access network (RAN) technologies continue to witness massive growth, with Open RAN gaining the most recent momentum. In the O-RAN specifications, the RAN intelligent controller (RIC) serves as an automation host. This article introduces principles for machine learning (ML), in particular, reinforcement learning (RL) relevant for the O-RAN stack. Furthermore, we review state-of-the-art research in wireless networks and cast it onto the RAN framework and the hierarchy of the O-RAN architecture. We provide a taxonomy of the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.). To address the challenges, we integrate a set of existing MLOps principles with unique characteristics when RL agents are considered. This paper discusses a systematic life-cycle model development, testing and validation pipeline, termed: RLOps. We discuss all fundamental parts of RLOps, which include: model specification, development and distillation, production environment serving, operations monitoring, safety/security and data engineering platform. Based on these principles, we propose the best practices for RLOps to achieve an automated and reproducible model development process.

Empowering the 6G Cellular Architecture With Open RAN

“…Additionally, closed-loop control facilitates the optimization of the RAN deployment itself [51,110,111]. Researchers have proposed zero-touch orchestration frameworks, fault-tolerant techniques, and efficient matching schemes between different RAN components, all contributing to better resource utilization and overall network efficiency [51,[112][113][114]. Finally, security of the RAN can also be enhanced through closed-loop monitoring and control, as we discuss in Sec.…”