TinyRIC-ML: A Lightweight Real Time ML Platform for O-RAN

With the open radio access network (O-RAN) movement driving the evolution of cellular networks towards 6G, real time (RT) RAN control and assurance has emerged as the next frontier in RAN programmability, with in-base station (BS) machine learning (ML) at its core. However, scalability demands associated with production-grade networks necessitate the need for a robust, yet lightweight in-BS ML operations framework to support automated ML workflows. To that end, this paper introduces TinyRIC-ML, a novel lightweight and high-performance ML platform for RT operations within the RAN. Key highlights include a comprehensive system architecture design, a concrete systems-level implementation, and a preliminary over-the-air experimental evaluation to demonstrate the system's performance and feasibility.