Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: A Survey
Abstract
Low earth orbit (LEO) satellite networks are emerging as important infrastructure for global connectivity and space-based sensing, while their rapidly varying geometry, heterogeneous measurements, and dynamic propagation environments create challenges for acquiring and exploiting spatial radio information. Spectrum cartography provides a framework for constructing and updating spatial representations of radio conditions from sparse observations. This survey reviews learning-based spectrum cartography for LEO satellite networks from an integrated inference--decision perspective. We organize the literature around three representative task categories: radio map reconstruction, localization and state estimation, and map-informed resource allocation. We further examine the progression from classical interpolation, statistical estimation, and structured optimization toward learning-based approaches that exploit environmental context, relational structure, generative modeling, and adaptive information fusion. Particular attention is given to attention mechanisms for learning query- and context-dependent interactions among irregular observations, while distinguishing learned relevance from calibrated measurement reliability. Representative formulations and illustrative examples clarify these modeling principles. Finally, we synthesize cross-cutting lessons and identify open challenges in uncertainty modeling, generalization, dynamic map updating, scalable inference, realistic evaluation, and tighter coupling between radio-map inference and network decision making.
Related
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BibTeX
@misc{tao2026learningbased,
title = {{Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: A Survey}},
author = {Liping Tao and Xindi Tong and Chee Wei Tan},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.10359},
eprint = {2605.10359},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.10359},
}
RIS
TY - UNPB
TI - Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: A Survey
AU - Liping Tao
AU - Xindi Tong
AU - Chee Wei Tan
PY - 2026
DA - 2026/10/18
PB - arXiv
DO - 10.48550/arxiv.2605.10359
UR - https://arxiv.org/abs/2605.10359
AB - Low earth orbit (LEO) satellite networks are emerging as important infrastructure for global connectivity and space-based sensing, while their rapidly varying geometry, heterogeneous measurements, and dynamic propagation environments create challenges for acquiring and exploiting spatial radio information. Spectrum cartography provides a framework for constructing and updating spatial representations of radio conditions from sparse observations. This survey reviews learning-based spectrum cartography for LEO satellite networks from an integrated inference--decision perspective. We organize the literature around three representative task categories: radio map reconstruction, localization and state estimation, and map-informed resource allocation. We further examine the progression from classical interpolation, statistical estimation, and structured optimization toward learning-based approaches that exploit environmental context, relational structure, generative modeling, and adaptive information fusion. Particular attention is given to attention mechanisms for learning query- and context-dependent interactions among irregular observations, while distinguishing learned relevance from calibrated measurement reliability. Representative formulations and illustrative examples clarify these modeling principles. Finally, we synthesize cross-cutting lessons and identify open challenges in uncertainty modeling, generalization, dynamic map updating, scalable inference, realistic evaluation, and tighter coupling between radio-map inference and network decision making.
ER -