B5G-OPEN: Beyond 5G – OPtical nEtwork coNtinuum

Nov. 2021 - Oct. 2024. Call: H2020-ICT-2020-2. IP(at UPC): Luis Velasco Esteban (coordinated from Telefonica)

B5G-OPEN targets the design, prototyping and demonstration of a novel end-to-end integrated packet-optical transport architecture based on MultiBand (MB) optical transmission and switching networks. MB expands the available capacity of optical fibres by enabling transmission within S, E, and O bands, in addition to commercial C and/or C+L bands, which translates into a potential 10x capacity increase and low-latency for services beyond 5G. To realize multiband networks, technology advances are required, both in data, control and management planes. Such technology advances complement novel packet-optical white boxes using flexible sliceable Bandwidth Variable Transceivers and novel pluggable optics. The availability of MB transmission will also lead to a complete redesign of the end-to-end architecture, removing boundaries between network domains and reducing electronic intermediate terminations.

The control plane will be extended to support multiband elements and a 'domain-less' network architecture. It will rely on physical layer abstraction, new impairment modelling, and pervasive telemetry data collection to feed AI/ML algorithms that will lead to a Zero-Touch networking paradigm including a full featured node operating system for packet-optical whiteboxes.

The results will be shown in two final demonstrations exposing the project benefits from operator and user perspectives. B5G-OPEN will have a clear impact on the society showing the evolution towards a world with increased needs of connectivity and higher capacity in support of new B5G services and new traffic patterns.

TRAINER: TowaRds fully AI-empowered NEtwoRks

Sept. 2021 - Aug. 2024. Call: Proyectos I+D+i 2020. IP: Salvatore Spadaro

The upcoming 6G services will dramatically increase requirements on many network Key Performance Indicators versus 5G, such as peak data rates, latencies and with ultra-high reliability. Actually, 5G and beyond networks (6G) have to support a combination of several types of workloads stemming from a variety of use cases/verticals.These workloads can come and go and may even change dynamically during services lifetime. As a result, the derived requirement from the networks may change often and these changes may be significant. Therefore, the networks must constantly adapt to and anticipate changes, increasing thus dramatically the network complexity. The observation that certain trends in network behavior can be predicted and actions taken in anticipation, leads to the introduction of AI/ML. Actually there is huge potential for Artificial Intelligence (AI) to improve management and performance of Beyond 5G networks which are expected to be developed in the years to come. Indeed, AI/ML technologies offer the potential to efficiently address the challenges of complex 5G and beyond networks. In particular, the TRAINER project will encompass different network segments (optical Metro/Access, Mobile Edge Computing (MEC) servers/central data centers). The ambitious innovation that TRAINER will bring reside on the concept of having AI/ML distributed at all levels of the SDN/NFV technology domain, including AI/ML-enabled end-to-end service orchestration, cognitive network management and even at optical data plane for quality of transmission assurance and signal processing.

Route56: Radio technologies for ubiquitous communications in the evolution from 5G to 6G

June 2020 - May 2023. Call: Proyectos I+D+i 2019. IP: Antonio Pascual Iserte, Josep Vidal Manzano

Future 6G applications will pose stringent requirements beyond 5G on spectral efficiency, ubiquitous coverage, end-to-end latency, reliability, and energy efficiency.

Accordingly, ROUTE56 has defined a set of objectives. Spectral super-efficiency requires the use of high-frequencies (mmWave or THz), the dense deployments of terminals, and new massive MIMO concepts. This demands the study of new channel models in the far- and near-fields, the blocking effects, extremely large antenna arrays, and the development of simple and accurate coverage predictors. In addition to terrestrial networks, those based on satellite and unmanned aerial vehicles will allow seamless connectivity.

Transceivers should exhibit high energy efficiency and yet low spectral efficiency loss. Also, opportunistic charging of batteries from ambient interference and through wireless power transfer (WPT) will increase the autonomy of terminals. A proper handover and mobility management assisted by network data based on distributed artificial intelligence (AI) can make a substantial difference to improve end-to-end latency.

Precise positioning in harsh scenarios, not possible in traditional networks, is needed. Dense cell-free networks with antenna arrays have an inherent potential to provide high resolvability 6-dimensional positioning. Sensing the environment and tracking users will enable ultra-short handover latencies and identify areas with battery-limited devices towards which WPT could be directed.

IBON: AI-powered Intent-Based packet and Optical transport Networks and edge and cloud computing for beyond 5G

Sept. 2021 - Aug. 2024. Call: Proyectos I+D+i 2020. IP: Luis Velasco Esteban

IBON will design and build a ubiquitous, secure and explainable Artificial Intelligence (AI)-powered intent-based networking (IBN) platform that spans end-to-end (from terminals to the RAN and transport network and from edge to cloud computing) and is aware of its state and context to autonomously take proactive actions for service assurance.

The IBN platform is integrated in a zero-touch control and orchestration platform featuring an AI Function Orchestrator to manage AI pipelines. The objective is to create an AI-assisted elastic and dynamic infrastructure supporting per-domain and e2e networks and services real time (RT) e2e operation automation, ensuring near-RT decision making and non-RT tight coordination. Specific components will be developed and integrated to create an agile platform that goes well beyond 5G and supports application-level resilience and intelligence through replication and elasticity. Demonstration will be carried out in an experimental environment.

The project will actively contribute to relevant standardization bodies and open source projects to promote IBON solutions to the wider community. IBON has the potential to create a significant shift in the way telecom services are commercialized, representing new market/higher volume opportunities for vendors, and tremendous potential for start-ups creating specialized applications.

MENTOR: Machine LEarning in Optical NeTwORks

Jan. 2021 - Dec. 2024.  Call: H2020-MSCA-ITN-2020. IP(at UPC): Luis Velasco Esteban (coordinated from Aston Univ.)

Optical fiber networks is one of the major drivers of our societal progress and a key enabler of the global telecommunication infrastructure. Optical networks underwent considerable changes over the past decade, as consequence of a continuous growth (exceeding 20% per year) of bandwidth demand. The current growth sets strong requirements in terms of capacity and costs for the operators, which seek to decrease the cost per transmitted bit. Several solutions have been proposed, and among them wide-band is more favorable to network operators. However, wide-band optical system presents new major challenges: optical components must guarantee similar performance over a broad spectrum, network optimization is carried out on a non-flat spectrum and with a much larger number of channels making design, optimization and control a complex problem. Therefore, application of machine learning (ML) techniques is of the growing importance for high-capacity multi-band (MB) optical systems. ML is becoming the technique of choice to solve complex nonlinear technical problems, such as, advance component design and management of wide-band networks. The European Industrial Doctorate MENTOR presents a timely proposal to train 6 ESRs in the interdisciplinary field of high industrial importance: ML applications in multi-band optical communications. As ML can properly work only when a large amount of real data is available, it is crucial to bring together academic partners and the industry that provide access to the data.

ARTIST: Smart Radio Access Integration of User Devices

Sept. 2021 - Aug. 2024. Call: Proyectos I+D+i 2020. IP: Oriol Sallent Roig

The vision of the smART radIo acceSs with inTegration of user devices (ARTIST) project is a Beyond 5G (B5G) scenario where the User Equipment (UE) is exploited not only to satisfy the specific needs of the UE owner but also to augment the Radio Access Network (RAN) infrastructure as a distributed capability and as a source of network intelligence. In other words, we envision the idea of UEs taking a more active role in network service provisioning as one of the key pillars to ground future mobile network evolution, to the extent that the very notion of a cell will have to be rethought as UEs will be able to actively complement the RAN infrastructure. This constitutes a radical paradigm shift from UEs operating only as Network Service Consumers towards embracing UEs also as Network Service Providers. That is, utilizing UEs as an extended computing, storage and networking element of the B5G RAN infrastructure as well as a central element for the realization of end-to-end connected network intelligence.

The ARTIST concept with its intrinsic connected intelligence will contribute to solving and/or mitigating a wide range of problematics that are inherent to wireless networks. Remarkably, the realization of ARTIST paves the way for enhanced service quality (e.g., enhanced coverage, better mobility management, user behavior and demand prediction), higher radio resource utilization efficiency (e.g., more efficient radio resource allocation, congestion control, better beam management), improved system performance (e.g., on device inference reduces network data traffic for more efficient mobility and spectrum utilization, better link adaptation can be attained through position[1]aware interference prediction) and simplified RAN deployment and operation (e.g., more capable Self Organizing Networks for e.g., mmWave network densification, reduced energy consumption).