R&I group

AI-driven Systems

The AI-driven Systems research group focuses on leveraging artificial intelligence and machine learning to address critical challenges in the sustainability and performance of next-generation networks, focusing on 6G virtualized RANs and Integrated Sensing and Communications (ISAC). By combining AI, energy optimization, and innovative sensing solutions, the AI-driven Systems research group contributes to making 6G networks sustainable, scalable, and capable of meeting the demands of advanced use cases in sensing and communications.

Research lines

  • AI-RAN: This research line aims to explore the limits of AI-driven network automation in future 6G systems. It aims to design sustainable 6G Virtualized Radio Access Networks by leveraging open and cloud-native technologies, possibly supported by smart surfaces. Researchers rely on analytical evaluations and experimental testing on real testbeds to validate their solutions.
  • ISAC: Integrated Sensing and Communications 6G Systems: This research aims to overcome current limitations in localization accuracy and the high cost of dedicated sensing systems. It leverages Digital Twins and focuses on the design of cost-efficient, in-band joint solutions to enhance both sensing and communication capabilities. Smart surfaces emerge as key technology enablers in this research line, supporting ISAC scenarios. Methodologies include analytical evaluation, simulations, and experimental testing, using mobile robots and drones.

Innovation lines

  • 3DSAR
  • 5GNSS
  • HachiGO

Technologies

  • Sustainable 6G virtualized RANs
  • 6G Integrated Sensing and Communications (ISAC)
  • Gen AI for RANs
  • AI/ML-driven 6G RAN Automation
  • vRANs Cost/Energy-efficiency
  • vRAN+Smart Surfaces
  • Cellular/RIS-based localization
  • Collaborative Mobile Robotics
  • Smart surfaces
  • Wireless XR/VR
  • Precision Medicine

AI-driven Systems

Publications

New publication at the IEEE Internet of Things Magazine

Bugelnig, D., Lemic, F., Veres-Vitályos, Á., Castillo Gómez-Raya, G., Costa-Pérez, X., Rinner B. “Adaptive Flight Planning for Neural 3D Reconstruction with Low-Altitude UAVs”, IEEE Internet of Things Magazine, 2026

RiLoCo: An ISAC-oriented AI Solution to Build RIS-empowered Networks

@ARTICLE{10988522, author={Encinas-Lago, Guillermo and Sciancalepore, Vincenzo and Wymeersch, Henk and Di Renzo, Marco and Costa-Pérez, Xavier}, journal={IEEE Transactions on Wireless Communications}, title={RiLoCo: An ISAC-Oriented AI Solution to Build RIS-Empowered Networks}, year={2025}, volume={24}, number={10}, pages={8221-8235}, keywords={Sensors;Reconfigurable intelligent surfaces;Performance evaluation;Location awareness;Measurement;Integrated sensing and communication;Wireless networks;Probability density function;Optimization;Millimeter wave communication;Reconfigurable intelligent surfaces (RISs);integrated sensing and communication (ISAC);reinforcement learning (RL);energy efficiency;6G mobile communication}, doi={10.1109/TWC.2025.3564832}}

COLoRIS: Localization-agnostic Smart Surfaces Enabling Opportunistic ISAC in 6G Networks

@article{encinas2025coloris, title={COLoRIS: Localization-agnostic Smart Surfaces Enabling Opportunistic ISAC in 6G Networks}, author={Encinas-Lago, Guillermo and Devoti, Francesco and Rossanese, Marco and Sciancalepore, Vincenzo and Di Renzo, Marco and Costa-P{‘e}rez, Xavier}, journal={IEEE Transactions on Mobile Computing}, year={2025}, publisher={IEEE} }

Energy-aware Joint Orchestration of 5G and Robots: Experimental Testbed and Field Validation

M. Groshev, L. Zanzi, C. Delgado, X. Li, A. d. l. Oliva and X. Costa-Pérez, “Energy-Aware Joint Orchestration of 5G and Robots: Experimental Testbed and Field Validation,” in IEEE Transactions on Network and Service Management, vol. 22, no. 4, pp. 3046-3059, Aug. 2025, doi: 10.1109/TNSM.2025.3555126. keywords: {Robots;Robot kinematics;5G mobile communication;Robot sensing systems;Sensors;Resource management;Real-time systems;Energy consumption;Testing;Peer-to-peer computing;5G;orchestration;robotics;optimization;offloading;energy efficient},

Quantum Computing in the RAN with Qu4Fec: Closing Gaps Towards Quantum-based FEC processors

Nikolaos Apostolakis, Marta Sierra-Obea, Marco Gramaglia, Jose A. Ayala-Romero, Andres Garcia-Saavedra, Marco Fiore, Albert Banchs, and Xavier Costa-Perez. 2025. Quantum Computing in the RAN with Qu4Fec: Closing Gaps Towards Quantum-based FEC processors. Proc. ACM Meas. Anal. Comput. Syst. 9, 2, Article 36 (June 2025), 25 pages. https://doi.org/10.1145/3727128

Curved Apertures for Customized Wave Trajectories: Beyond Flat Aperture Limitations

J. M. Canals, F. Devoti, V. Sciancalepore, M. D. Renzo and X. Costa-Pérez, “Curved Apertures for Customized Wave Trajectories: Beyond Flat Aperture Limitations,” in IEEE Wireless Communications Letters (2025).

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AI-driven Systems

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