System advantages of Raman Amplifiers

The theory of Raman amplification is briefly reviewed together with the definition of Noise-Figure for distributed amplification. Erbium-Doped Fiber Amplifiers and Raman Amplifiers are compared on the basis of their non-linear impact. An optimal configuration for Hybrid Raman/Erbium-Doped Fiber Amplifier is derived for the design of multi-span systems. Results obtained through the analytical formalisms are compared with accurate simulation result

Electronic equalization for advanced modulation formats in dispersion-limited systems

We investigate in this letter the use of electronic equalization on dispersion limited systems for different modulation formats. Besides analyzing equalization on standard NRZ intensity modulation as a reference, we focus on two advanced modulation formats, Duobinary and Differential Phase Shift Keying (DPSK) which are recently gaining large attention. We demonstrate that the introduction of an electronic equalizer strongly improves standard NRZ performance, whereas it has a limited effect on Duobinary and DPSK formats. Moreover, we give rules for the optimal choice of the equalizer transversal filter parameters, i.e., the number of taps and the delay between taps

Propagation impairments due to Raman effect on the coexistence of GPON, XG-PON, RF-video and TWDM-PON

We analyze propagation effects in the coexistence of GPON, XG-PON, RF-Video and TWDM-PON. We show that high power TWDM-PON channels excite Stimulated Raman Scattering inducing extra-loss on GPON due to power depletion. We address the problem through simulations and propose and validate a simple analytical model for the effec

Cross-Train: Machine Learning Assisted QoT-Estimation in Un-used Optical Networks

The quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. TThe quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. Typically, the network administrator configures the network element (NE) working point according to the specified nominal values given by vendors. These operational NEs experienced some variation from the given nominal working point and thus put up uncertainty during their operation, resulting in the introduction of uncertainty in estimating LP QoT. Consequently, a substantial margin is required to avoid any network outage. In this context, to reduce the required margin provisioning, a machine learning (ML) based framework is proposed which is cross-trained using the information retrieved from the fully operational network and utilized to support the QoT estimation unit of an un-used sister network

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

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