Federico Lozano‑Cuadra

Ph.D. Researcher in AI for Space Communications

Federico works on machine learning for space communications, with a focus on Low Earth Orbit satellite constellations, autonomous lunar rover swarms, and decentralized intelligent networks. His work spans multi-agent reinforcement learning, foundational models, and communication-aware AI for autonomous space systems.

Malaga, Spain · California, USA · Baltimore, USA

Affiliations

University of Malaga · NASA Jet Propulsion Laboratory · Johns Hopkins University ACE Lab

Research

Learning-based lunar rover exploration

Federico develops communication-aware AI for autonomous lunar rover swarms, combining foundation models with multi-agent reinforcement learning and graph attention to support decentralized routing and decision making in delay-tolerant networks. This work is carried out with NASA Jet Propulsion Laboratory and Johns Hopkins University ACE Lab.

Multi-agent deep reinforcement learning for distributed satellite routing

This line of work studies decentralized routing in LEO satellite constellations using multi-agent deep reinforcement learning, including continual learning under changing network conditions. Federico co-developed an open-source simulator used to train, evaluate, and compare routing policies in dynamic satellite topologies.

SatCom-TELMA/MA-DRL_Routing_Simulator — —
Simulation demo of multi-agent deep reinforcement learning routing in a satellite constellation

Multicast traffic optimization in mesh satellite constellations

Federico works on joint routing and downlink resource allocation for multicast traffic in mesh non-terrestrial networks, aiming to improve delivery efficiency across interconnected LEO constellations. The focus is on scalable optimization methods for shared traffic demands in dynamic satellite mesh architectures.

Publications

Google Scholar

Journals

  • Continual deep reinforcement learning for decentralized satellite routing

    F. Lozano-Cuadra, B. Soret, I. Leyva-Mayorga, and P. Popovski. IEEE Transactions on Communications, 2025. DOI: 10.1109/TCOMM.2025.3562522.

    Scholar IEEE arXiv

Conferences

  • Learning decentralized routing policies via graph attention-based multi-agent reinforcement learning in lunar delay-tolerant networks

    F. Lozano-Cuadra, B. Soret, M. Sanchez Net, A. Cauligi, and F. Rossi. Accepted for presentation at the International Symposium on Planetary Robotics (iSpaRo), Sendai, Japan, 2025.

    Scholar arXiv

  • An open source multi-agent deep reinforcement learning routing simulator for satellite networks

    F. Lozano-Cuadra, M. Thorsager, I. Leyva-Mayorga, and B. Soret. In Proceedings of SPAICE2024: The First Joint European Space Agency / IAA Conference on AI in and for Space, pp. 420-424, Sep. 17-19, 2024, ESA European Centre for Space Applications and Telecommunications (ECSAT), Oxford, UK. DOI: 10.5281/zenodo.13885645. GitHub: SatCom-TELMA/MA-DRL_Routing_Simulator.

    Scholar Zenodo arXiv

  • Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing

    F. Lozano-Cuadra and B. Soret. In 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Stockholm, Sweden, 2024, pp. 1-2. DOI: 10.1109/ICMLCN59089.2024.10624767.

    Scholar IEEE arXiv

  • Q-learning for distributed routing in LEO satellite constellations

    B. Soret, I. Leyva-Mayorga, F. Lozano-Cuadra, and M. D. Thorsager. In 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Stockholm, Sweden, 2024, pp. 208-213. DOI: 10.1109/ICMLCN59089.2024.10624807.

    Scholar IEEE arXiv

Posters

  • Resource allocation for multicast transmission in mesh non-terrestrial networks

    F. Lozano-Cuadra, I. Leyva-Mayorga, J. J. Nielsen, and B. Soret. Poster presentation at the Danish National Space Conference, Aalborg University, Aalborg, Denmark, 2025.

Under review

  • Decentralized graph attention-based multi-agent reinforcement learning for communications in autonomous lunar rover swarms

    F. Lozano-Cuadra, B. Soret, M. Sanchez Net, A. Cauligi, and F. Rossi. Under review, 2026.

  • Routing and Downlink Resource Allocation for Multicast Traffic in LEO Satellite Constellations

    F. Lozano-Cuadra, I. Leyva-Mayorga, J. J. Nielsen, and B. Soret. Under review, 2026.