TRAINER – TowaRds fully AI-empowered NEtwoRks
(PID2020-118011GB-C22)
2021 – 2024
Role: Principal Investigator
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. 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-B 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.
GENIUS – Empowering Clean-energy Transition of Energy Intensive Industry Through Advanced and Innovative Digital Tools
2021 – 2024
Role: Senior Researcher
The main objective of GENIUS is to develop a complete digital twin (DT) embedded in an IoT platform running in the cloud for the 1) advanced monitoring and control, 2) state estimation and prediction, 3) active energy management of assets, systems and infrastructures, 4) monitoring of carbon footprint and 5) dynamic economic analysis of energy intensive industries in urban environments. These DT enable the development of innovative and effective tools and decision support systems based in artificial intelligence (IA) for optimising energy consumption, operation, dimensioning, cost effectiveness and lifecycle of the energy assets and infrastructures as a whole, by maximising the synergies of local renewable power generation and electric vehicle fleets, towards the full decarbonisation of the entire value chain of these industries.
TIMING – Towards a Smart and efficient Telecom Infrastructure meeting current and future industry needs
2021 – 2024
Role: Senior Researcher
TIMING targets to design a solution to enable e2e reliable deterministic services supported by operators’ infrastructures that are currently carrying on a best effort basis. TIMING 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.