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TELEFONICA INNOVACION DIGITAL SL

Country: Spain

TELEFONICA INNOVACION DIGITAL SL

90 Projects, page 1 of 18
  • Funder: European Commission Project Code: 101210495
    Funder Contribution: 194,075 EUR

    With the rapid evolution of Artificial Intelligence, distributed machine learning methods such as Federated Learning (FL) are becoming ubiquitous in present-day technology. In FL, devices train neural network models while data stays local. A central entity then aggregates the model updates into a global model. Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to participate in this learning framework. In a nutshell, SL splits the model into parts, and allows clients (devices) to offload the largest part as a processing task to a computationally powerful helper (edge server, cloud, or other devices). Essentially, SL is a paradigm shift offering a more flexible version of FL that alleviates the load at the devices by better utilizing other available resources in the network. However, this method comes with optimization challenges since networking decisions need to be made in order to orchestrate the SL operations and overcome any communication overhead. Despite the increasing attention towards SL, current algorithms focus on minimizing the training time and improving on energy efficiency, without however bearing any performance guarantees. In order to make SL efficient, OPALS will fill this crucial gap by providing algorithms with provable guarantees. In particular, OPALS focuses on 3 main research axes. First, it studies the well-established problem of minimizing the training time, in search of the first algorithm with guarantees. Second, it seeks ways of leveraging SL to reduce the carbon footprint of distributed learning. Third, it investigates how SL could be employed in a decentralized setting in view of the increasing importance of swarm intelligence. OPALS will employ mathematical modelling and cutting-edge optimization methods to achieve these goals. As a result, OPALS will pave the way to better resource utilization, and thus, efficient SL exploitable for technological innovation.

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  • Funder: European Commission Project Code: 101167288
    Overall Budget: 1,999,710 EURFunder Contribution: 1,999,710 EUR

    Emerging U-Space and UAM concepts envisage a new generation of small, highly manoeuvrable, and highly automated aircraft operating at low altitude, alongside existing helicopter and general aviation users. Coordination & deconfliction of large numbers of such aircraft operating in primarily urban environments requires new Communications, Navigation, and Surveillance (CNS) infrastructure to ensure safety of passengers, the public, and other stakeholders while supporting complex low-altitude operations. Leveraging the scalable waveforms of 5G New Radio (NR), modern IP-based software-defined networking, and distributed computing capabilities, ANTENNAE (dAta driveN cosT-Effective 5G iNtegrated CommuNication, Navigation, and Surveillance (CNS)-as-A-ServicE) proposes a flexible and resilient integrated CNS-as-a-Service model supporting both low-altitude piloted and U-Space operations, and builds upon the mature and growing family of 3GPP 5G standards including system architecture, deployment models, and commercial implementations. ANTENNAE will apply advanced modelling to validate the applicability of 3GPP standards to deliver low-altitude CNS functions, including the full range of aeronautical data services (through 5G eMBB & URLLC), navigation (through 5G-based A-PNT), and surveillance (through emerging A-SUR and joint communication & sensing (JCS) concepts). ANTENNAE will examine the architectural benefits of established 5G deployment models for providing distributed data services, network resilience, and scalability. ANTENNAE will also look to the future of the 3GPP standards by examining technologies under development in the 3GPP working groups for beyond 5G ("6G”) services. Finally, ANTENNAE will conduct a rigorous quantitative techno-economic analysis informed by these engineering models to assess the financial feasibility of deploying a scalable integrated CNS-as-a-Service through a 5G access network, with comparison to alternative technological approaches.

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  • Funder: European Commission Project Code: 101071147
    Overall Budget: 4,485,660 EURFunder Contribution: 4,485,660 EUR

    We live in an era of information overload that impairs objective decision making, especially in time-sensitive contexts. Information Visualization (InfoVis) systems have been used to mitigate information overload, yet they have not yet unlocked their potential in critical decision-making scenarios. From emergency rooms and autonomous cars to operational command centres, a clear understanding and rapid assessment based on the available data can make the difference between life and death. SYMBIOTIK envisions an effortless interaction dialogue between human and InfoVis systems to support decision making processes, inspired by known biological principles and guided by artificial intelligence (AI). Critically, this dialogue requires AI solutions with context awareness, emotion sensing, and expressing capabilities. We propose a novel framework where both the human and the machine cooperate towards a common goal and evolve together. Awareness principles will allow us to engineer complex systems, making them more resilient and more human-centric. We will define an integrative approach for awareness engineering and propose a specific open source implementation. Finally, we will demonstrate and validate the role and added-value of such an awareness framework in two scenarios: supporting novice-to-expert transitions and critical decision making. The awareness principles to be developed in this project can support learning, adaptation, and self-development of intelligent systems over long periods of time, not only in the InfoVis domain. Therefore, SYMBIOTIK has potential to achieve the real breakthroughs needed to bring awareness and emotional intelligence for decision-making tasks in computing systems. The results of the project will benefit a range of stakeholders, from human vision and brain researchers, computer scientists, citizens, as well as research funding bodies and policy makers.

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  • Funder: European Commission Project Code: 101226491
    Funder Contribution: 4,305,000 EUR

    The overall research goal of SpecX is to provide a high-level research programme and doctoral training to 15 Doctoral Candidates (DCs) in large-scale spectrum measurements, analyses, and applications in future telecom infrastructure. The goal is to create a research and innovation workforce with transferable and sustainable skills in applied mathematics, radio hardware, cellular infrastructure, edge computing, spectrum data collection, artificial intelligence, spectrum management methods, and business aspects to assess and analyse big spectrum data and provide innovative services. This goal will be achieved by a unique combination of local and networkwide research training designed to provide the DCs with the needed fundamental elements to conduct the research programme for measuring real spectrum data, analysing it, developing innovative methods, and inventing new valuable applications. Hands-on in-depth training will be strengthened with non-academic placements, as well as multidisciplinary, intersectoral, and international cooperation, to maximise the employability of DCs and the project’s impact in the short- and long-term.

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  • Funder: European Commission Project Code: 101192369
    Overall Budget: 3,188,940 EURFunder Contribution: 2,979,970 EUR

    6G-Machine Intelligence based Radio Access Infrastructure (6G-MIRAI) aims at developing reliable and robust AI-native wireless communication systems that enable the practical exploitation of the full potential of the latest physical layer technological advances, especially cell-free massive MIMO, and of next-generation virtualized and potentially disaggregated radio access networks (RANs). To achieve this goal, 6G-MIRAI will rely on extensive know-how from established industry players and academic partners from both the EU and Japan. The overall goal of 6G-MIRAI is deconstructed into four main objectives: (O1) Reliable and robust AI-ML techniques for future wireless communications; (O2) Practical AI-native design of next-generation radio access networks; (O3) Common EU-JP platform for data, benchmarking, and validation; and (O4) Aligned EU-JP strategy on future standardization efforts. These objectives align with the EU HORIZON-JU-SNS-2024-STREAM-B-01-05 call requirements, focusing on the evolution of Radio Access Networks (RAN) for 6G to pave the way for future advancements towards AI-native radio access networks. All four objectives will contribute to aligning views on the radio interface and RAN concepts between the EU and Japan, and between main industry and academic institutions in the EU. 6G-MIRAI objectives will be achieved through five interconnected research and technology items: (RTI1) Realistic channel and hardware models to enable the AI-native air interface; (RTI2) Practical AI-based physical layer designs; (RTI3) Scalable AI-based network coordination; (RTI4) Evolved AI-ready architecture designs; and (RTI5) AI-oriented data management, testing, and proof-of-concept. These RTIs will enrich the technological portfolio of all involved partners and the 6G ecosystem, since they will have a direct impact on standardization, IP generation, and scientific publications.

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