Invited talk by Takaya Miyazawa (NICT) at SmartCom 2026 on Multi-Non-RT RIC Orchestration for Network-Wide Management of Integrated O-RAN and NTN.
Authors: Takaya Miyazawa *, Ved P. Kafle *, Ngo Van Mao **, Tony Quek **, and Kentaro Ishizu *
(* NICT / ** Singapore University of Technology and Design (SUTD))
Open Radio Access Network (O-RAN) has emerged as a promising paradigm for realizing intelligent, open, and programmable radio access networks. Despite its potential, significant challenges remain in improving network energy efficiency and optimizing end-to-end service performance. The Non-Real-Time RAN Intelligent Controller (Non-RT RIC) provides a powerful framework for addressing these challenges through AI/ML-driven automation and network optimization. At the same time, the integration of terrestrial networks (TNs) and non-terrestrial networks (NTNs) has attracted growing interest as a means of extending network coverage and ensuring seamless quality-of-service (QoS) provisioning.
To address energy-efficiency requirements, we previously proposed an energy-efficient O-RAN power management architecture that dynamically controls base station (BS) activation and deactivation using AI/ML while leveraging NTN resources to maintain service continuity. Our investigation revealed that, while such an approach is effective for local optimization, broader end-to-end optimization across heterogeneous networks requires coordination among multiple control domains. This limitation extends beyond energy-efficient network operation and also affects traffic steering, resource management, and distributed AI lifecycle management across heterogeneous network domains.
In this presentation, we introduce a vision, architecture, and operational framework for a multi-Non-RT RIC orchestration system that enables network-wide energy-efficient management, cross-domain resource optimization, intelligent traffic steering, and distributed AI lifecycle management. Building upon this vision, we provide an overview of our research and development activities. First, we review research efforts on energy-efficient power management for O-RAN base stations by jointly utilizing AI/ML and NTN resources, and discuss future research directions. Second, we present recent advances in TN-NTN convergence technologies, including architectures and operational scenarios for integrated and distributed network control systems that support end-to-end service design and orchestration, performance monitoring, and resource management across both TN and NTN environments. In addition, we introduce joint demonstration activities conducted by the National Institute of Information and Communications Technology (NICT) and the Singapore University of Technology and Design (SUTD), which were exhibited at Mobile World Congress (MWC) 2025 and MWC 2026. These demonstrations highlight the practical feasibility and benefits of intelligent, energy-efficient, and converged network management based on O-RAN, NTN, and AI/ML technologies.