Roberto Armellin

dblp:150/1683 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0002-3516-6428ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 100%
Computer networks
1 paper
Cellular and mobile networks · 77% Vehicular, aerial and satellite networks · 12% Physical-layer communications · 12%

Topics — the 8 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
control lyapunov function
0.912025
Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control › robot control › optimal control
linear quadratic regulator
0.912025
Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control
robot control
0.912025
Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations · IEEE Trans. Robotics 2025
Cellular and mobile networks
6g
0.812024
On the Use of Mega Constellation Services in Space: Integrating LEO Platforms Into 6G Non-Terrestrial Networks · IEEE J. Sel. Areas Commun. 2024
Cellular and mobile networks › 6g
non-terrestrial networks
0.812024
On the Use of Mega Constellation Services in Space: Integrating LEO Platforms Into 6G Non-Terrestrial Networks · IEEE J. Sel. Areas Commun. 2024
Robotics › Motion planning and robot control › robot control
learning control
0.312025
Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations · IEEE Trans. Robotics 2025
Physical-layer communications › modulation
adaptive modulation and coding
0.212024
On the Use of Mega Constellation Services in Space: Integrating LEO Platforms Into 6G Non-Terrestrial Networks · IEEE J. Sel. Areas Commun. 2024
Vehicular, aerial and satellite networks
satellite communication
0.212024
On the Use of Mega Constellation Services in Space: Integrating LEO Platforms Into 6G Non-Terrestrial Networks · IEEE J. Sel. Areas Commun. 2024

Methods — techniques the papers use, named apart from their topics

reinforcement learning · 0.9control lyapunov function · 0.9LQR · 0.9monte carlo simulation · 0.8
YearPublicationVenuePosition
2025 Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations
Harry Holt, Roberto Armellin
IEEE Trans. Robotics2
2024 On the Use of Mega Constellation Services in Space: Integrating LEO Platforms Into 6G Non-Terrestrial Networks
abstract
This paper presents a framework for integrating Low-Earth Orbit (LEO) platforms with Non-Terrestrial Networks (NTNs) in the emerging 6G communication landscape. Our work applies the Mega-Constellation Services in Space (MCSS) paradigm, leveraging LEO mega-constellations’ expansive coverage and capacity, designed initially for terrestrial devices, to serve platforms in lower LEO orbits. Results show that this approach overcomes the limitation of sporadic and time-bound satellite communication links, a challenge not fully resolved by available Ground Station Networks and Data Relay Systems. We contribute three key elements: (i) a detailed MCSS evaluation framework employing Monte Carlo simulations to assess space user links and distributions; (ii) a novel Space User Terminal (SUT) design optimized for MCSS, using different configurations and 5G New Radio Adaptive Coding and Modulation; (iii) extensive results demonstrating MCSS’s substantial improvement over existing Ground Station Networks and Data Relay Systems, motivating its role in the upcoming 6G NTNs. The space terminal, incorporating a multi-system, multi-orbit, and software-defined architecture, can handle Terabit-scale daily data volumes and minute-scale latencies. It offers a compact, power-efficient solution for properly integrating LEO platforms as space internet nodes.
Gabriel Maiolini Capez, Mauricio A. Cáceres, Roberto Armellin, Christopher P. Bridges, Juan A. Fraire, Stefan Frey, Roberto Garello
IEEE J. Sel. Areas Commun.3