An ultra-fast method for gain and noise prediction of Raman amplifiers

A machine learning method for prediction of Raman gain and noise spectra is presented: it guarantees high-accuracy (RMSE < 0.4 dB) and low computational complexity making it suitable for real-time implementation in future optical networks controllers

Modulation Format, Core and Spectrum Assignment in a Multicore Optical Link with and without MIMO Receivers

We study the modulation format, core and spectrum assignment problem in a multi-core flexi-grid optical link, under the assumption that MIMO receivers can operate on various core subsets and considering distance-adaptive reaches for different modulation formats. We show results obtained following an optimization approach, comparing scenarios with and without the use of MIMO transmission

Statistical Analysis of GSNR Fluctuations Due to Physical Layer Uncertainties

We present an analytical model based on the uncertainty propagation theory for the generalized signal-to-noise ratio (GSNR) error estimation at the output of an optical line system due to connector loss and amplifier gain ripple uncertainties. The results are validated by comparison with a Monte Carlo analysis, showing an excellent agreement in terms of estimated GSNR average and standard deviation

Deep Learning-Driven Extraction of Superluminescent Diodes Parameters

We present a deep learning-based method for the automatic extraction of physical parameters from optical spectra and power values of a chirped, tapered, dual-section quantum dot superluminescent diode. The neural network is able to estimate a set of parameters that are capable of reproducing the behavior of the target device with high accuracy

Decision directed carrier phase estimation with a limiter for coherent dense wavelength divison multiplexing systems

Various example embodiments are disclosed. According to one example embodiment, a phase error is estimated in a series of digital symbols of a phase-modulated signal, where the signal is subject to a non-linear phase shift error due to transmission of the signal through an optical fiber. A phase correction of an instant digital symbol that succeeds the series of digital symbols is estimated, where the estimated phase correction is based on the estimated phase errors in the series of digital symbols. The estimated phase correction of the instant digital symbol is limited to a maximum absolute value, and the estimated phase correction is applied to the instant digital symbol of the signal

Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers

We experimentally validate a real-time machine learning framework, capable of controlling the pump power values of Raman amplifiers to shape the signal power evolution in two-dimensions (2D): frequency and fiber distance. In our setup, power values of four first-order counter-propagating pumps are optimized to achieve the desired 2D power profile. The pump power optimization framework includes a convolutional neural network (CNN) followed by differential evolution (DE) technique, applied online to the amplifier setup to automatically achieve the target 2D power profiles. The results on achievable 2D profiles show that the framework is able to guarantee very low maximum absolute error (MAE) (<0.5 dB) between the obtained and the target 2D profiles. Moreover, the framework is tested in a multi-objective design scenario where the goal is to achieve the 2D profiles with flat gain levels at the end of the span, jointly with minimum spectral excursion over the entire fiber length. In this case, the experimental results assert that for 2D profiles with the target flat gain levels, the DE obtains less than 1 dB maximum gain deviation, when the setup is not physically limited in the pump power values. The simulation results also prove that with enough pump power available, better gain deviation (less than 0.6 dB) for higher target gain levels is achievable

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Determinar a cuantos pacientes le realizamos prácticas recomendadas en promoción y prevención de la salud y pesquisa de enfermedades prevalentes en atención primaria.Determine to how many patients we carried out best practices in health promotion, prevention or investigation of prevalent diseases in primary care.Fil: Gasull, Andrea. Hospital Luis Lagomaggiore (Mendoza, Argentina). Servicio de Clínica MédicaFil: Lascano, Soledad. Hospital Luis Lagomaggiore (Mendoza, Argentina). Servicio de Clínica MédicaFil: Matile, Carlos. Hospital Luis Lagomaggiore (Mendoza, Argentina). Servicio de Clínica MédicaFil: Salomon, Susana. Hospital Luis Lagomaggiore (Mendoza, Argentina). Servicio de Clínica MédicaFil: Carena, José. Hospital Luis Lagomaggiore (Mendoza, Argentina). Servicio de Clínica Médic

16x125 Gb/s Quasi-Nyquist DAC-Generated PM-16QAM Transmission Over 3590 km of PSCF

We report on a transmission experiment over high-performance pure silica core fiber (PSCF) of 16 Nyquist wavelength-division-multiplexed (Nyquist-WDM) channels at a symbol rate of 15.625 GBaud, using polarization-multiplexed (PM) 16 symbols quadrature amplitude modulation (16QAM), resulting in a per-channel raw bit rate of 125 Gb/s. The channel spacing is 16 GHz, corresponding to 1.024 times the symbol rate. The interchannel crosstalk penalty is drastically reduced through the confinement of the signal spectrum within a near-Nyquist bandwidth, achieved with digital filtering and digital-to-analog converters (DACs) operating at 1.5 samples/symbol. The optical line is a recirculating loop composed of two spans of high-performance PSCF with erbium-doped fiber amplifiers only. The transmission distance of 3590 km at a target line bit-error rate (BER) of 1.5 10^-2 is achieved at a raw spectral efficiency (SE) of 7.81 b/s/Hz. Assuming a commercial hard forward error correction with 20.5% redundancy, capable of handling the target BER, the net SE is 6.48 b/s/Hz, the highest so far reported for multithousand kilometer transmission of PM-16QAM at ≥ 100 Gb/s per channel. These results demonstrate the feasibility of very high SE DAC-enabled ultra-long-haul quasi-Nyquist-WDM transmission using PM-16QAM with current technologies and manageable digital signal processing complexit