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Automated Heuristic Design for Network Operations

Network operation relies on heuristics to solve many tasks rapidly and efficiently across the protocol stack. These heuristics are the...

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RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks

Disculpa, pero esta entrada está disponible sólo en English. Quantum networks are becoming increasingly important because of advancements in quantum...

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Science for Industry - S4i 2026

IMDEA Networks participa en la nueva edición de Science for Industry, una iniciativa de la UAM y BeAble Capital destinada...

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Presentation of the RIXISAC project: Reflective intelligent surface-assisted Intelligent and eXplainable Integrated Sensing and Communications

In the future of communication, Sixth Generation (6G) networks promise unprecedented connectivity, driving innovations in smart health, vehicular networks, and...

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Enhancing Cellular Network Positioning Through 5G Technologies for Hybrid Systems and Emergency Scenarios

Fifth-generation cellular networks are introducing new capabilities to significantly enhance positioning accuracy through wideband reference signals specifically designed for time-based...

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The Secret Life of Research Projects

Research projects have a secret life that most PhD students and postdoctoral researchers are not always aware of. Beyond scientific...

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Analog and Digital Wireless Communications for Computations

The growing demand for running machine learning (ML) services over wireless networks has stimulated the development of new communication paradigms...

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Beyond QKD: Emulating and Orchestrating General Entanglement Networks

Until now, most quantum network deployments have focused primarily on QKD, one of the most mature quantum technologies, where the...

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Terahertz Communications for Next-Generation Satellite Networks: From Channel Models to Orbit

The next generation of satellite networks will operate at unprecedented data rates, reaching beyond 100 Gbps per link to support...

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Model-Aware BitTorrent for Robust and Bandwidth-Efficient Decentralized Federated Learning

Federated Learning (FL) enables training over sensitive, distributed data without centralizing it, but the canonical server-based architecture introduces a single...

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