To achieve these technical objectives, ONFIRE deploys a three-year research programme centred on two European industrial PhDs.
Individual Research Project on data plane technologies for disaggregated optical networks
Fabiano Locatelli (ESR1) joined on May 2018. He has been involved in WP1 activities (disaggregated optical networks). ESR1 objectives included the investigation of novel cost-effective subsystems for non-intrusive in-band optical performance monitoring (OPM), agnostic to the optical transmission waveforms. To this aim, we theoretically studied a front-end monitoring scheme, able to deliver arbitrary spectral resolution, that could work regardless of the modulation format of the considered monitored signals. The possibility to integrate the proposed monitoring scheme with a specific monitoring agent, also allowed us to address another of the main ONFIRE objectives, which was the integration of the developed data plane technologies with the control plane. We also proposed a closed control loop approach to optimize specific figures of merit related to the superchannel subchannels. Another of the objectives addressed by ESR1 within the ONFIRE project was the identification of specific techniques to extract from the monitored physical layer information adequate figures of merit to be leveraged by the control plane.
Individual Research Project on cognitive optical networks
Ankush Mahajan (ESR2), joined ONFIRE project on September 2018. He has been involved on conducting WP2 activities (cognitive optical networks). ESR2 objectives includes physical layer monitoring schemes, ML and data mining techniques to leverage massive physical layer monitoring information to adjust and re-optimize the network settings for aggregated (traditional) and next generation disaggregated optical transport networks. For effective optical network planning, operation and optimization, it is necessary to estimate the Quality of Transmission (QoT) of the connections. Network designers are interested in accurate and fast QoT estimation for services to be established in a future or existing network. Typically, QoT estimation is performed using a Physical Layer Model (PLM) which is included in the QoT estimation tool or Qtool. A design margin is generally included in a Qtool to account for the modeling and parameter inaccuracies, to re-assure an acceptable performance. The PLM with the addition of a design margin is typically referred to as the QoT estimation tool or Qtool. PLM accuracy is highly important as modeling errors translate into a higher design margin which in turn translate into wasted capacity or unwanted regeneration. In this regard, ESR2 first outlined the limitations on applying ML to assist traditional Qtools. Furthermore, with the aim of improving the Qtool estimation accuracy in multi-vendor networks, ESR2 proposed PLM extensions. ESR2 also aimed at investigating and solving the issue of accuracy limitation of Qtool in dynamic optimization tasks.
