Performance Evaluation of a Reconfigurable Optical Add Drop Multiplexer Design for High-Order Regular and Offset-QAM Signals

In this paper, we investigate the performance of a Reconfigurable Optical Add Drop Multiplexer (ROADM) architecture, that is suitable of supporting high-order regular as well as offset based QAM signals. In the proposed design, a bank of Phase Sensitive Amplification (PSA) subsystems is introduced for processing offset QAM signals, while sub-channel extraction is achieved via waveform splitting and coherent subtraction. Numerical studies have been carried out to validate functional performance, when processing high-order regular and offset based QAM signals

Information theory analysis of regenerative channels

In this paper we summarize our recently proposed work on the information theory analysis of regenerative channels. We discuss how the design and the transfer function properties of the regenerator affect the noise statistics and enable Shannon capacities higher than that of the corresponding linear channels (in the absence of regeneration)

Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems

We investigate the application of dynamic deep neural networks for nonlinear equalization in long haul transmission systems. Through extensive numerical analysis we identify their optimum dimensions and calculate their computational complexity as a function of system length. Performing comparison with traditional back-propagation based nonlinear compensation of 2 steps-per-span and 2 samples-per-symbol, we demonstrate equivalent mitigation performance at significantly lower computational cost

Sparse Identification for Nonlinear Optical communication systems

We have developed a low complexity machine learning based nonlinear impairment equalization scheme and demonstrated its successful performance in SDM transmission links achieving compensation of both inter- and intra- channel Kerr-based nonlinear effects. The method operates in one sample per symbol and in one computational step. It is adaptive, i.e. it does not require a knowledge of system parameters, and it is scalable to different power levels and modulation formats. The method can be straightforwardly expanded to multi-channel systems and to any other type of nonlinear impairment

Perturbative discrete-time multivariate fiber channel model with finite memory

We introduce a discrete-time fibre channel model that provides an accurate analytical description of signal-signal and signal-noise interference with memory defined by the interplay of nonlinearity and dispersion. Also the conditional pdf of signal distortion, which captures non-circular complex multivariate symbol interactions, is derived providing the necessary platform for the analysis of channel statistics and capacity estimations in fibre optic links

A Reconfigurable OADM Architecture for High-Order Regular- and Offset- QAM based OFDM Super-Channels

We investigate the performance of a Reconfigurable All-Optical Add Drop Multiplexer (ROADM) architecture, that is suitable of supporting high-order regular and offset based QAM signals. The design is based on a highly modular interferometric, structure, in which OFDMA sub-channels remain in the optical domain. Sub-channel extraction is achieved via waveform splitting and coherent subtraction. Numerical studies have been carried out to validate performance and optimize critical parameters of the add/drop process. Particularly, input parameters like the signal extinction ratio, pulse rise time, switching window and input power levels have been optimized for single sub-carrier add/drop operation, from an OFDM super-channel comprising seven optical sub-carriers with 10 GHz spacing. Results have shown that the proposed ROADM architecture can accommodate and process both regular and offset based high-order QAM signals

Nonlinear Equalization in Long Haul Transmission Systems Using Dynamic Multi-Layer Perceptron Networks

In this paper we investigate the application of dynamic multi-leyer perceptron networks for long haul transmission systems showing performance improvement and significant superiority of neural network complexity over digital back-propagation method

Mitigation of Amplitude and Phase Distortions by Using Conjugate-NOLM Regenerator

In this paper we propose a conjugate nonlinear optical loop mirror scheme (Conj-NOLM) by cascading two NOLMs with an intermediate optical phase conjugator stage (OPC). This new configuration utilizes mid-span spectral inversion to cancel out the nonlinear phase distortion that is introduced by the two NOLM units. Moreover, numerical investigation has been carried out for 16-QAM signals demonstrating increased robustness against accumulated amplitude and phase distortions in the transmission links

Digital Back Propagation Performance in Spatial Multiplexing Systems

We review digital methods to mitigate the Kerr nonlinearity in multi-mode and/or multi-core fibres operating in different operational regimes as determined by differential mode delay and linear mode coupling. The results demonstrate that transmission performance can be more than doubled for feasible fibres characteristics

Nonlinear Compensation Using Digital Back-Propagation in Few-Mode Fibre Spans with Intermediate Coupling

We investigate for the first time the performance of virtual back-propagation using multimode Manakov equations, derived for the weak- and strong-coupling regimes, after forward-propagation using a fully stochastic model over all linear coupling regimes