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

• URL: http://arxiv.org/abs/2111.06978v1
• Date: Fri, 12 Nov 2021 22:57:09 GMT
• Title: RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN
• Authors: Peizheng Li, Jonathan Thomas, Xiaoyang Wang, Ahmed Khalil, Abdelrahim Ahmad, Rui Inacio, Shipra Kapoor, Arjun Parekh, Angela Doufexi, Arman Shojaeifard, Robert Piechocki
• Abstract summary: This article introduces principles for machine learning (ML), in particular, reinforcement learning (RL) relevant for the Open RAN stack. We provide a taxonomy of the challenges faced by ML/RL models throughout the development life-cycle. 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.
• Score: 4.279828770269723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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