Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link.
In this work we explore numerically an experimentally the dependence of the broadened spectra on the choice of fibers and we analyze a series of basic rules to be taken into account when using nonlinear broadening to reduce the gain ripple of broadband Raman amplifiers
We have developed a theory of power budget optimization in dispersion-managed cascaded optical amplifier systems. It can be used for a variety of systems and can be easily generalized for more complex configurations. Applying our general results to a specific configuration we analyzed a novel design of the transmission system with periodically imbalanced in-line amplification.
This study analyzes the validity of different Q-factor models in the BER estimation in RZ-DPSK transmission at 40 Gb/s channel rate. The impact of the duty cycle of the carrier pulses on the accuracy of the BER estimates through the different models has also been studied.
High‐frequency nonlinear solitary wave propagation in media with strongly varying dispersion is considered. A dynamical model is proposed which allows us to determine the propagation characteristics of a strongly dispersion‐managed soliton without applying any averaging procedure. This approach is compared with previously developed models, focusing on the dependence of the energy on the quasimomentum. An analogy with a macroscopic nonlinear quantum oscillator model is briefly discussed. The analytical results are confirmed by numerical simulations.
We discover dramatic impact of vortex formation in the transverse component of the Poynting vector of the fundamental core mode in solid core micro — structured optical fibers on the energy dissipation. The vortices can reduce losses of the mode by several orders of magnitude under proper selection of the fiber parameters at a given wavelength.
Summary form only given. Fibre lasers are known to provide a rich tapestry of operational regimes, which can be attributed to the nonlinear nature of light dynamics in optical fibre at high powers, and the multidimensional system parameter space. Given their inherent complexity, identifying and discerning the underlying physical processes that gives rise to them still remains a formidable challenge. Here, for the first time in experiment, we show how the Nonlinear Fourier Transform (NFT) (see e.g. [1-3] and references therein) can be used as an effective tool for the identification and classification of lasing regimes. The NFT provides a framework for identification of coherent structures (nonlinear multi-soliton modes) embedded into dispersive radiation [2, 3].
Recently, a new design of a model-locked all-fibre Figure-8 laser employing a nonlinear amplifying loop mirror (NALM) with two active fibre segments and two independently controlled pump-power modules has been proposed and experimentally demonstrated. This laser layout combines the reliability and robustness of conventional Figure-8 lasers with the flexibility of nonlinear-polarisation-evolution (NPE) lasers, providing access to a variety of generation regimes with a relatively wide adjustment range of the pulse parameters. Moreover, it enables reliable and reproducible live electronic adjustment of the lasing regimes, which is practically impossible to do by adjusting fibre-based polarisation controllers in NPE lasers. The general issue of reaching a target mode-locked laser regime with a setup featuring many adjustable parameters can be intelligently addressed by using machine-learning techniques. Here, we apply predictive regression to find optimum operating regimes in the NALM laser that are accessible through independent control of the pump powers in the gain segments, Pp,1, Pp,2, and the laser output coupling ratio β. We use a piece-wise propagation model for generating data that characterises the laser. In the fibres, propagation follows a standard modified nonlinear-Schrodinger equation including gain saturation and spectral response for the active segments. The gain coefficient amplitude is dependent on the average signal and pump powers, the average power dynamics being described by standard rate equations. We have trained a gradient boosted tree algorithm on our dataset to identify high-energy, stable mode-locked solutions across the full variation range of the total pump level delivered to the active fibres, Pp,tot, the ratio Pp1/Pp,tot, and β (tens of thousands of points). The algorithm has quickly handled the whole parameter space. Our approach paves the way for alternative approaches to the optimisation of nonlinear cavity dynamics, and can be generalised to other complex systems and higher degrees of freedom.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)