Team

Xavier Costa

Scientific Director & Director - AI-Driven Systems Research Group

Contact xavier.costa@i2cat.net
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Publications

Nuberu: Reliable RAN Virtualization in Shared Platforms.

Garcia-Aviles, G. and Garcia-Saavedra, A. and Gramaglia, M. and Costa-Perez, X. and Serrano, P. and Banchs, A., “Nuberu: Reliable RAN Virtualization in SharedPlatforms.” In:The 25th Annual International Conference on Mobile Com-puting and Networking. 2021, pp. 1–16.

Graph Neural Networks as an Enabler of Terahertz-based Flow-guided Nanoscale Localization over Highly Erroneous Raw Data

Bartra, G.C., Lemic, F., Pascual, G., Rodas, A.P., Struye, J., Delgado, C. and Pérez, X.C., 2024. Graph Neural Networks as an Enabler of Terahertz-based Flow-guided Nanoscale Localization over Highly Erroneous Raw Data. IEEE Journal on Selected Areas in Communications. IEEE early access: https://ieeexplore.ieee.org/document/10528284. arXiv open access: https://arxiv.org/abs/2307.05551.

CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing

Leonardo Lo Schiavo, Gines Garcia-Aviles, Andres Garcia-Saavedra, Marco Gramaglia, Marco Fiore, Albert Banchs, and Xavier Costa-Perez. 2024. CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing. In Proceedings of the 30th Annual International Conference on Mobile Computing and Networking (ACM MobiCom '24). Association for Computing Machinery, New York, NY, USA, 558–572. https://doi.org/10.1145/3636534.3649381

Attacking O-RAN Interfaces: Threat Modeling, Analysis and Practical Experimentation

Pau Baguer, Girma M. Yilma, Esteban Municio, Gines Garcia-Aviles, Andres Garcia-Saavedra, Marco Liebsch, and Xavier Costa-Pérez. "Attacking O-RAN Interfaces: Threat Modeling, Analysis and Practical Experimentation". IEEE Open Journal of the Communications Society. 2024

Tiki-Taka: Attacking and Defending Deep Learning-based Intrusion Detection Systems

Chaoyun Zhang, Xavier Costa-Perez, and Paul Patras. 2020. Tiki-Taka: Attacking and Defending Deep Learning-based Intrusion Detection Systems. In Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop (CCSW'20). Association for Computing Machinery, New York, NY, USA, 27–39. DOI:https://doi.org/10.1145/3411495.3421359