LEO Small-Satellite Constellations for 5G and Beyond-5G Communications
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Abstract (arXiv)
The next frontier towards truly ubiquitous connectivity is the use of Low Earth Orbit (LEO) small-satellite constellations to support 5G and Beyond-5G (B5G) networks. Besides enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC), LEO constellations can support ultra-reliable communications (URC) with relaxed latency requirements of a few tens of milliseconds. Small-satellite impairments and the use of low orbits pose major challenges to the design and performance of these networks, but also open new innovation opportunities. This paper provides a comprehensive overview of the physical and logical links, along with the essential architectural and technological components that enable the full integration of LEO constellations into 5G and B5G systems. Furthermore, we characterize and compare each physical link category and explore novel techniques to maximize the achievable data rates.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 4; 2025: 1; 2026: 2.
10 papers.
- 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
- 2022Federated Learning in Satellite Constellationscited by 21
- 2024On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networkscited by 4
- 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
- 2024Scheduling for On-Board Federated Learning with Satellite Clusterscited by 2
- 2024Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routingcited by 2
- 2025Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networkscited by 1
- 2026Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communicationcited by 1
- 2023Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communicationscited by 0
- 2026FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellationscited by 0
- 2026FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations10 Aug 2026
- 2026Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication24 Jan 2026
- 2025Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks27 Jan 2025
- 2024On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks23 Sep 2024
- 2024Energy-Aware Federated Learning in Satellite Constellations23 Sep 2024
- 2024Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing27 Feb 2024
- 2024Scheduling for On-Board Federated Learning with Satellite Clusters14 Feb 2024
- 2023Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communications13 Mar 2023
- 2022Federated Learning in Satellite Constellations1 Jun 2022
- 2021Ground-Assisted Federated Learning in LEO Satellite Constellations3 Sep 2021