Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G

TL;DR

This work addresses the problem of enabling ubiquitous, reliable 6G connectivity via AI-powered satellite-based Non-Terrestrial Networks (NTNs). It surveys NTN fundamentals, identifies key challenges (e.g., long delays, Doppler, spectrum sharing), and maps AI approaches (ML/DL, RL/DRL, FL, SpL) to these challenges across physical, data-link, and upper layers. The contributions include a taxonomy of AI-NTN research thrusts, a synthesis of current AI testbeds and O-RAN-based integration efforts, and a discussion of practical implementation hurdles with actionable insights and future directions. The paper underscores the potential of AI to transform NTN into a core 6G enabler, while highlighting constraints such as onboard compute limits, aging information, overhead, security, and data quality that must be addressed for real-world deployment. The results suggest that a combination of online, distributed, and low-complexity AI architectures, integrated with O-RAN RIC, can deliver real-time, scalable NTN control and optimization, advancing satellite-terrestrial convergence toward $1 ext{ Tbps}$ peak data rates and $ ext{μs}$-level latency in future networks.

Abstract

Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th Generation (6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge as the primary enabler for NTN, leveraging their extensive coverage, stable orbits, scalability, and adherence to international regulations. However, satellite-based NTN presents unique challenges, including long propagation delay, high Doppler shift, frequent handovers, spectrum sharing complexities, and intricate beam and resource allocation, among others. The integration of NTNs into existing terrestrial networks in 6G introduces a range of novel challenges, including task offloading, network routing, network slicing, and many more. To tackle all these obstacles, this paper proposes Artificial Intelligence (AI) as a promising solution, harnessing its ability to capture intricate correlations among diverse network parameters. We begin by providing a comprehensive background on NTN and AI, highlighting the potential of AI techniques in addressing various NTN challenges. Next, we present an overview of existing works, emphasizing AI as an enabling tool for satellite-based NTN, and explore potential research directions. Furthermore, we discuss ongoing research efforts that aim to enable AI in satellite-based NTN through software-defined implementations, while also discussing the associated challenges. Finally, we conclude by providing insights and recommendations for enabling AI-driven satellite-based NTN in future 6G networks.

Figures (20)

• Figure 1: Structure of the paper.
• Figure 2: An illustration of different NTN components in 6G.
• Figure 3: General communication architecture for satellite-based NTN.
• Figure 4: Propagation loss for satellites at different heights and with different carrier frequencies.
• Figure 5: Generic ML model.