Towards a smart and efficiency telecom infrastructure meeting current and future industry needs

January 2022 – December 2024
TSI-063000-2021-145, TSI-063000-2021-148, TSI-063000-2021-149

Identifying Industry 4.0 as a key vertical, TIMING targets to design a solution to enable e2e reliable TSN services supported by operators’ infrastructures that are currently carrying on a best effort basis. TIMING consists of three subprojects: SP-1, SP-2, and SP-3.

TIMING-SP1 will analyze TSN support in the Ethernet and Wi-Fi segments and identify the enhancements to be made for supporting the sub-millisecond latency for TSN systems. Scheduling solutions to support these enhancements will be devised. In addition, solutions to automate the deployment of e2e TSN services with assured performance will be designed. Such service automation relies on a control plane, which will include a TSN controller able to control and monitor Ethernet and Wi-Fi TSN nodes, a TSN Connectivity Manager to provide e2e connectivity across TSN and non-TSN segments, and a QoS estimation tool that includes accurate TSN traffic models. Finally, TIMING SP-1 will maximize impact by influencing major vendors and service providers on the adoption of the developed principles through communication, dissemination, and standardization activities, while exploiting the results and knowledge obtained, and contributing to the digital transition of the industry and the green deal.

TIMING SP-2 will develop solutions to support TSN in the Ethernet and Wi-Fi segments ensuring the sub-millisecond latency. Scheduling solutions to support these enhancements will be developed and assessed. In addition, solutions to automate the deployment of e2e TSN services with assured performance will be developed and assessed. Such service automation relies on a control plane, which will include a TSN controller able to control and monitor Ethernet and Wi-Fi TSN nodes, a TSN Connectivity Manager to provide e2e connectivity across TSN and non-TSN segments, and a QoS estimation tool that includes accurate TSN traffic models. In TIMING SP-2 the different components will be integrated and to build to validate the whole architecture.

TIMING SP-3 will build PoC demonstrators validating the whole TIMING architecture. The POC will demonstrate: (1) at the modeling level, a tool that evaluates the performance of the new TSN service to be deployed and the impact on the existing services (TSN and/or BE traffic); (2) at the control plane, the capability to deploy reliable e2e TSN services with committed performance in terms of e2e delay; and (3) at the infrastructure level, the capability to transport TSN traffic between two TSN domains: one with Automated Guided Vehicles (AGVs) emulating a factory, and the other with the AGV’s controller, which requires bounded latency communications with the AVs; other services for loading the system will be also included.

Towards fully AI-empowered networks

September 2021 – March 2025
PID2020-118011GB-C21

The huge effort of several worldwide R&D public and private initiatives towards the design of 5G networks and systems carried out in the last few years is significantly contributing to the digital transformation of economy and society. Currently, network operators are provisioning initial 5G services and continuing the long process of network evolution for future advanced 5G services. However, the digital transformation increased demands on the network side as well as its increased capacity. Moreover, 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.

5G and beyond networks 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.

Leveraging on the wide expertise of two multi-disciplinary research groups, The TRAINER project ambitiously aims at architecting and validating a converged an AI/ML empowered end-to-end network sustainable infrastructure towards the full deployment of beyond 5G networks towards 6G. In particular, the TRAINER solution will encompass different network segments (Core, optical Metro/Access, Multi-access Edge Computing (MEC) servers/central data centers and 5G-RAN), different network technologies and business roles. The ambitious innovation that TRAINER will bring reside on the concept of having AI/ML distributed at all levels of the future networks, including AI/ML-enabled end-to-end service orchestration, cognitive network management in SDN/NFV, LISP and RINA technology domains and even at optical data plane level for quality of transmission assurance and signal processing.

Intelligent Data Science and Artificial Intelligence (IDEAI) research group

January 2021 – June 2025
2021 SGR 01532

The IDEAI research group is the common place at the Universitat Politècnica de Catalunya – BarcelonaTech (UPC) for both, long-experienced and excellence-background newbies research groups in AI and Intelligent Data Science. The CBA sub-group is part of IDEAI and has been a consolidated research group of the Catalan Government Generalitat de Catalunya (2005 SGR 00481 for the period 2005-2008, 2009 SGR 1140 for the period 2009-2013, 2014 SGR 1427 for the period 2014-2016, and 2017 SGR 01037 for the period 2017-2020).