AI-Open-RAN for Non-Terrestrial Networks

TL;DR

This paper introduces AIO-RAN-NTN, a unified AI-enabled open architecture for Non-Terrestrial Networks, enhancing interoperability and flexibility in next-generation telecoms, supported by a testbed and AI-based KPI forecasting.

Contribution

It proposes a novel integrated AIO-RAN-NTN architecture applying open interfaces and AI functionalities to NTNs, with experimental validation using a 5G testbed and AI models.

Findings

AIO-RAN-NTN improves interoperability and flexibility.

AI-based KPI forecasting mitigates mobility-related limitations.

Testbed results validate the architecture's effectiveness.

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.