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We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes' destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas. [ABSTRACT FROM AUTHOR]

With the advancement in evolving concepts of software‐defined networks and elastic‐optical‐network, the number of design parameters is growing dramatically, making the lightpath (LP) deployment more complex. Typically, worst‐case assumptions are utilized to calculate the quality‐of‐transmission (QoT) with the provisioning of high‐margin requirements. To this aim, precise and advanced estimation of the QoT of the LP is essential for reducing this provisioning margin. In this investigation, we present convolutional‐neural‐networks (CNN) based architecture to accurately calculate QoT before the actual deployment of LP in an unseen network. The proposed model is trained on the data acquired from already established LP of a completely different network. The metric considered to evaluate the QoT of LP is the generalized signal‐to‐noise ratio (GSNR). The synthetic dataset is generated by utilizing well appraised GNPy simulation tool. Promising results are achieved, showing that the proposed CNN model considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin. [ABSTRACT FROM AUTHOR]

Presents a method for the evaluation of bit error rate (BER) in optical systems where the received noise can have an arbitrary spectral shape. Assumptions for the method of evaluating the BER system; Algorithms used to implement the method; Validation of the method.

The constant need to improve network capacity and reduce power consumption in current fixed-grid WDM networks motivates researchers to find alternative solutions other than replacing the deployed equipment. One of the possible solutions is the use of hybrid EDFA/Raman amplifier, which improves signal-to-noise-ratio in a point-to-point link. This work aims at quantifying, by means of a detailed power model, the effect of these novel ideas on the power consumption of the network, which is key to build a future green Internet. In particular, we consider the use of a randomly generated topology of 20 nodes to study the effect of hybrid amplification, fiber type, fiber span length, and sensitivity to average link length and average traffic per node. Results show that hybrid amplification in moderate pumping regime minimizes total network power consumption for networks made up of all 3 fiber types in particular for nonzero dispersion-shifted fiber. [ABSTRACT FROM AUTHOR]

The authors study the performance of pulse-amplitude modulation, with 2, 4 and 8 levels per symbol, for short reach applications with propagation over up to 2 km of standard single mode fibre. They consider the three modulation formats operated both at the same bit-rate of 25 Gbps and at same symbol rate of 25 Gbaud, supposing 3.125% forward error-correction overhead and bit error rate threshold of 10-4. They analyse transmitters (Tx) and receivers (Rx) based on digital signal processing, including a training-driven finite impulse response (FIR) filter equaliser set through the least-mean-square algorithm. They analyse the sensitivity impairments induced by a limited Tx/Rx electrical bandwidth and evaluate the minimum number of taps - and consequent latency - needed by the FIR equaliser to obtain sensitivity benefits. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for global internet traffic, together with evolving concepts of software-defined networks and elastic-optical-networks, demand not only the total capacity utilization of underlying infrastructure but also a dynamic, flexible, and transparent optical network. In general, worst-case assumptions are utilized to calculate the quality of transmission (QoT) with provisioning of high-margin requirements. Thus, precise estimation of the QoT for the lightpath (LP) establishment is crucial for reducing the provisioning margins. We propose and compare several data-driven machine learning (ML) models to make an accurate calculation of the QoT before the actual establishment of the LP in an unseen network. The proposed models are trained on the data acquired from an already established LP of a completely different network. The metric considered to evaluate the QoT of the LP is the generalized signal-to-noise ratio (GSNR), which accumulates the impact of both nonlinear interference and amplified spontaneous emission noise. The dataset is generated synthetically using a well-tested GNPy simulation tool. Promising results are achieved, showing that the proposed neural network considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin. Furthermore, we also analyze the impact of cross-features and relevant features training on the proposed ML models' performance. [ABSTRACT FROM AUTHOR]

The increasing trend of global IP traffic is mainly driven by high-definition video services and cloud computing and storage. Moreover, to maintain a high quality of service in content delivery networking, data are geographically replicated in data centers distributed within network topologies. Thus, data centers are an emerging scenario for research and development aimed at energy-efficient transmission and networking solutions. Previous research work has focused on intradata center energy efficiency while interdata center energy issues have not been extensively analyzed yet. We propose heuristics and meta-heuristics for optimal placement of data centers with minimum power consumption over a network topology relying on flex-grid spectral use. In order to minimize the network's power consumption, we have performed a detailed comparison of heuristic and meta-heuristic designs for different network scenarios based on real topologies. Moreover, our results show that meta-heuristic provides an optimum data center placement in a reasonable amount of time for a small- to medium-sized network. [ABSTRACT FROM AUTHOR]