Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: An Overview

TL;DR

This survey reviews learning-based spectrum cartography techniques for LEO satellite networks, emphasizing attention mechanisms for adaptive measurement fusion amid dynamic orbital and propagation conditions.

Contribution

It provides a comprehensive overview of attention-based learning methods for spectrum cartography in LEO networks, highlighting their role in adaptive and reliable measurement fusion.

Findings

Attention mechanisms enable flexible fusion of heterogeneous measurements.

Simulation studies demonstrate the effectiveness of the proposed framework.

The survey offers a unified perspective on measurement-driven inference in LEO networks.

Abstract

Low earth orbit (LEO) satellite networks are emerging as a key infrastructure for global connectivity and space-based sensing. Many tasks in such systems can be formulated as measurement-set-to-spatial-inference problems, where spatial variables are inferred from sparse and heterogeneous wireless observations. Spectrum cartography provides a unifying framework for this paradigm, encompassing representative tasks such as satellite-assisted localization and radio map reconstruction, as well as map-informed resource allocation. Yet the highly dynamic orbital geometry, complex propagation conditions, and reliability-varying nature of LEO measurements pose fundamental challenges for traditional model-driven and interpolation-based methods. This article surveys the literature from 1964 to 2026 on learning-based spectrum cartography as applied to LEO satellite networks, with a particular focus on attention mechanisms as a principled operator for adaptive and reliability-aware measurement fusion across localization, radio map reconstruction, and resource allocation tasks. We review modeling foundations and key challenges of representative tasks, and analyze how attention-based learning enables flexible fusion of heterogeneous measurements for both inference and map-informed decision-making. Representative formulations and simulation studies are provided to illustrate the framework and demonstrate its effectiveness, offering a unified perspective for measurement-driven inference and decision-making in LEO satellite networks.