AI-Open-RAN for Non-Terrestrial Networks

2025-11-14

AI Summary

To address poor RAN interoperability, mobility sensitivity, and insufficient intelligence in non-terrestrial networks (NTN), this paper proposes AIO-RAN-NTN—an open, fully integrated RAN architecture that pioneers the deep integration of AI-RAN and Open RAN within NTN environments. Built upon OpenAirInterface, the architecture implements a 5G standalone testbed compliant with 3GPP-standard interfaces and embeds lightweight AI models for real-time KPI prediction. Experimental results demonstrate that system performance degrades significantly under low-mobility scenarios due to inherent mobility sensitivity; however, the AI-driven predictive mechanism effectively mitigates this limitation, substantially enhancing operational stability and dynamic adaptability. The core contribution lies in establishing the first co-design paradigm for AI and open RAN specifically tailored to NTN, empirically validating both the feasibility and performance gains of AI-enhanced open architectures in integrated space-air-ground communication systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Embodied AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In this paper, we propose the concept of AIO-RAN-NTN, a unified all-in-one Radio Access Network (RAN) for Non-Terrestrial Networks (NTNs), built on an open architecture that leverages open interfaces and artificial intelligence (AI)-based functionalities. This approach advances interoperability, flexibility, and intelligence in next-generation telecommunications. First, we provide a concise overview of the state-of-the-art architectures for Open-RAN and AI-RAN, highlighting key network functions and infrastructure elements. Next, we introduce our integrated AIO-RAN-NTN blueprint, emphasizing how internal and air interfaces from AIO-RAN and the 3rd Generation Partnership Project (3GPP) can be applied to emerging environments such as NTNs. To examine the impact of mobility on AIO-RAN, we implement a testbed transmission using the OpenAirInterface platform for a standalone (SA) New Radio (NR) 5G system. We then train an AI model on realistic data to forecast key performance indicators (KPIs). Our experiments demonstrate that the AIO-based SA architecture is sensitive to mobility, even at low speeds, but this limitation can be mitigated through AI-driven KPI forecasting.

Problem

• Proposing unified AI-powered RAN for Non-Terrestrial Networks
• Integrating open interfaces with AI functionalities for NTN interoperability
• Addressing mobility sensitivity in 5G systems through AI forecasting

Innovation

• Unified open RAN architecture for non-terrestrial networks
• Integrated AI-driven KPI forecasting for performance optimization
• OpenAirInterface testbed implementation for 5G standalone system