Giacomo Borraccini is a Postdoctoral Scientist in the Optical Networking and Sensing Department at NEC Laboratories America. He received his Master of Science in Electronic Engineering and his PhD in Electric, Electronic & Telecommunication Engineering from Politecnico di Torino. His research focuses on physical-layer fiber-optic networks, with an emphasis on system-level optical-transmission design, modelling, and control. At NEC, Dr. Borraccini contributes to the development of open and disaggregated optical network systems, spanning different scenarios and use cases.In an age defined by massive data consumption, the next generation of optical networks will target enhanced scalability to accommodate increasing capacity demands, ensuring rapid deployment and seamless integration. Targeting those challenges, he plays a key role in NEC’s collaborative efforts to define automatic procedures based on telemetry, incorporating automation and cognition into network management. His work includes automated physical-layer characterization and optimization of single- and multi-band amplified systems (both lumped and distributed) using analytical models, machine learning, and numerical optimization algorithms.
We report the first trial of network tomography over a live network in a multi-domain environment. We visualize end-to-end optical powers along multiple routes across multiple domains solely from a commercial B00G transponder, enabling performance bottleneck localization, power and routing optimization, and lightpath provisioning.
We experimentally investigated variable spectral loading in an OMS, identifying performance under best and worst transmission conditions. Metrics and data visualization allowed correlation between channel configurations and OSNR variations, enabling the derivation of a simple spectrum allocation rule.
We introduce anchor vectors to monitor Rayleigh-backscattering variability in a fiber-optic computing system that performs nonlinear random projection for image classification. With a ~0.4-s calibration interval, system stability can be maintained with a linear decoder, achieving an average accuracy of 80%-90%.
This tutorial presents an architecture and methods for a/1-photonics networks-as-a-service in distributed Al data center infrastructures. We discuss server-based coherent transceiver architectures, remote transponder control, rapid end-to-end lightpath provisioning, digital longitudinal monitoring, and line-system calibration, demonstrating their feasibility through field validations.
Optical transmission networks require intelligent traffic adaptation and efficient spectrum usage. We present scalable machine learning (ML) methods for network performance modeling, andfield trials of distributed fiber sensing and classic optical network traffic coexistence.
This paper outlines QoT-driven optimization strategies in coherent fiber-optic WDM networks, addressing distinct transmission scenarios, QoT metrics, control-plane methodologies, and emerging trends to enhance network reliability, flexibility and capacity.
Optical transmission systems require accurate modeling and performance estimation for autonomous adaption and reconfiguration. We present efficient and scalable machine learning (ML) methods for modeling optical networks at component- and network-level with minimizeddata collection.
We propose building a spectrally resolved QoT Digital Twin for optical network domains where models and telemetry are unavailable, by probing transmission on a singlespectral slot, using GNPy, and demonstrating accurate experimental results.
A differential algorithm is proposed to calibrate the physical digital model of an optical line system from scratch at the commissioning phase, using minimal measurements and maximizing signal and OSNR estimation accuracy.
We implement a cascaded learning framework leveraging three different EDFA and fiber component models for OSNR and GSNR prediction, achieving MAEs of 0.20 and 0.14 dBover a 5-span network under dynamic channel loading.
