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This research proposes a holistic architecture for 6G LEO satellite IoT, leveraging deep learning for multibeam precoding and massive grant-free random access to enhance connectivity. The study validates these approaches through an on-orbit verification platform, addressing critical gaps in uplink efficiency, downlink robustness, and inter-satellite link resilience. By integrating terrestrial networks with space-based infrastructure, the work establishes a foundational pathway for ubiquitous global IoT coverage.
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LEO Satellite Internet of Things: Architecture, Technology, and On-Orbit Verification

arXiv2 min read

arXiv:2609.20165v1 Announce Type: cross Abstract: Low Earth orbit (LEO) satellite constellations are poised to become a cornerstone of the sixth-generation (6G) Internet of Things (IoT), providing truly global coverage and ubiquitous connectivity. This article presents a holistic two-dimensional system architecture for 6G LEO satellite IoT that incorporates composition and functional perspectives to facilitate the seamless integration of LEO satellites and terrestrial networks. Building upon this architecture, we evaluate three pivotal enabling technologies targeting the uplink, downlink, and inter-satellite links (ISLs). Specifically, we analyze massive grant-free random access for efficient uplink connectivity, investigate deep learning-based multibeam precoding for robust downlink transmission, and examine distributed cooperative routing for resilient ISL data delivery. Furthermore, we present an on-orbit verification platform that validates the real-world feasibility and performance of the proposed solutions. Finally, we outline key open challenges and future research directions to guide the realization of future LEO satellite IoT.

Abstract:Low Earth orbit (LEO) satellite constellations are poised to become a cornerstone of the sixth-generation (6G) Internet of Things (IoT), providing truly global coverage and ubiquitous connectivity. This article presents a holistic two-dimensional system architecture for 6G LEO satellite IoT that incorporates composition and functional perspectives to facilitate the seamless integration of LEO satellites and terrestrial networks. Building upon this architecture, we evaluate three pivotal enabling technologies targeting the uplink, downlink, and inter-satellite links (ISLs). Specifically, we analyze massive grant-free random access for efficient uplink connectivity, investigate deep learning-based multibeam precoding for robust downlink transmission, and examine distributed cooperative routing for resilient ISL data delivery. Furthermore, we present an on-orbit verification platform that validates the real-world feasibility and performance of the proposed solutions. Finally, we outline key open challenges and future research directions to guide the realization of future LEO satellite IoT.

Information Theory (cs.IT); Machine Learning (cs.LG)

arXiv:2609.20165 [cs.IT]

(or arXiv:2609.20165v1 [cs.IT] for this version)

https://doi.org/10.48550/arXiv.2609.20165 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 239 words

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