Nokia (Italy)

The increasing complexity and capacity demands of modern optical communication networks require advanced tools for predictive maintenance and fault prevention. This study presents a systematic methodology for generating a comprehensive network failure database using Digital Twins (DTs) based on the open-source optical network simulation framework GNPy (Gaussian Noise model in Python). GNPy enables the generation of synthetic datasets to support the development of Machine Learning (ML) models for proactive fault detection and network performance optimization. By modeling a wide range of failure scenarios such as amplifier degradation, span loss variation, and component misconfigurations, GNPy enables detailed analysis of their impact on critical transmission parameters including Generalized Signal-to-Noise Ratio (GSNR), Optical Signal-to-Noise Ratio (OSNR), and channel power. The proposed methodology allows the generation of a diverse and scalable database that reflects both normal and degraded network operating conditions. This synthetic dataset provides valuable input for training ML algorithms to detect subtle performance degradations and predict potential failures before they impact running services. By leveraging this simulation-based data generation approach, network operators can significantly enhance the reliability and resilience of their transport networks. Furthermore, the integration of GNPy with data-driven ML models lays the foundation for intelligent network management systems capable of automated fault prediction and proactive maintenance strategies in future high-capacity optical networks.

Forecasting state of polarization (SOP) in aerial fibers poses several challenges due to exposure to dynamic und unpredictable environmental factors, such as wind-induced vibrations, leading to multi-timescale SOP variations. Traditional statistical and deterministic approaches may struggle to accurately model the complex, nonlinear, and multi-scale dynamics of these SOP fluctuations, making them less effective for real-world scenarios. To address these challenges, this study proposes a weather-adaptive forecasting framework combining discrete wavelet transform and artificial neural networks to predict SOP changes across varying timescales. By integrating environmental data, the model effectively captures rapid fluctuations and gradual drifts, significantly improving forecasting accuracy. Validation using field data across two locations demonstrates the model's reliability for both short-term and long-term predictions. Including environmental factors namely wind speed, temperature, and humidity improves long-term forecasting accuracy by over 64.5% compared to models taking as input SOP change data alone. Wind gusts enhance short-term forecasting under extremely windy conditions. Our proposed framework outperforms conventional techniques, offering a reliable framework for SOP change prediction and advancing proactive optical system monitoring.

We report on ultrafast photonic integrated detectors with bandwidth exceeding 180 GHz. The device is fabricated using a CVD-grown large angle twisted-bilayer graphene as active material. We demonstrate the use of this device as a photonic-aided D-band upconverter operating at 140 GHz carrier frequency, performing 10Gb/s QPSK and 4Gb/s 16-QAM signals transmission.

Anomaly detection in optical networks is vital for network reliability. We investigate leveraging state of polarization (SOP) data and computer vision for this task, despite challenges like scarce anomaly data. Our study explores using deep learning to identify anomalies in optical networks, focusing on SOP time series derived images data. Challenges such as anomaly rarity are addressed through innovative approaches. Our research sheds light on the potential of combining SOP data and computer vision for robust anomaly detection in optical networks, while also highlighting the ongoing efforts to overcome associated challenges and propel the field forward.

The performance of a real fifth generation base station was studied by using a reverberation chamber as a real life propagating environment. Preliminary tests were conducted in order to define 5G base station operation conditions at mmwave and emulated scenarios where reconfigurable intelligent surface(s) (RISs) will successively be tested. Measurements campaign was carried out under the H2020 European project RISE6G and a collaboration program between TIM S.p.A., Nokia and Università Politecnica delle Marche.

Network Management as Code is presented to manage the infrastructure and deployment of control functions in a declarative manner by adopting GitOps in open disaggregated optical networks. We introduce an Automation Engine that analyzes topology changes to trigger the Continuous Delivery procedure of SDN applications on a micro-service architecture.

This paper provides an overview of the missing pieces currently preventing effective application of machine learning in the field. We discuss access to field data and we perform a proof of concept for the two SDN automation use cases based on programmable hardware, open APIs and streaming telemetry. The automation workflow with its performance evaluations is also presented

We present a novel end-to-end autoencoder-based learning for coherent optical communications using a "parallelizable" perturbative channel model. We jointly optimized constellation shaping and nonlinear pre-emphasis achieving mutual information gain of 0.18 bits/sym./pol. simulating 64 GBd dual-polarization single-channel transmission over 30x80 km G.652 SMF link with EDFAs.

We examine how the ultimate performance of WDM networks is affected when the wavelength assignment strategy aims at minimizing their number of colorless/directionless add/drop blocks, and so why contentionless optical node layout is preferable.

The mobile data traffic increase and the future connection of billions of Internet of Things (IoT) devices require operators to reshape the existing transport network architecture. Today, more than 50 % of Base-stations (BTS) are backhauled via radio. Radio technology can continue to play this vital role in future transport networks if it is able to evolve to cope with the new capacity level and latency requirements supporting the new 5G services. In this paper, a possible answer to this demand is provided, proposing a radio solution working in D-Band (130-170 GHz) and enabling a reconfigurable meshed network that can support the backhaul needs of future 5G and beyond networks.