No abstract

The capacity in space division multiplexing (SDM) systems with coupled channels is fundamentally limited by mode-dependent loss (MDL) and mode-dependent gain (MDG) generated in components and amplifiers. In these systems, MDL/MDG must be accurately estimated for performance analysis and troubleshooting. Most recent demonstrations of SDM with coupled channels perform MDL/MDG estimation by digital signal processing (DSP) techniques based on the coefficients of multiple-input multiple-output (MIMO) adaptive equalizers. Although these methods provide a valid indication of the order of magnitude of the accumulated MDL/MDG over the link, MIMO equalizers are usually updated according to the minimum mean square error (MMSE) criterion, which is known to depend on the channel signal-to-noise ratio (SNR). Therefore, MDL/MDG estimation techniques based on the adaptive filter coefficients are also impaired by noise. In this paper, we model analytically the influence of the SNR on DSP-based MDL/MDG estimation, and show that the technique is prone to errors. Based on the transfer function of MIMO MMSE equalizers, and assuming a known SNR, we calculate a correction factor that improves the estimation process in moderate levels of MDL/MDG and SNR. The correction factor is validated by simulation of a 6mode long-haul transmission link, and experimentally using a 3-mode transmission link. The results confirm the limitations of the standard estimation method in scenarios of high additive noise and MDL/MDG, and indicate the correction factor as a possible solution in practical SDM scenarios.

We experimentally validate a mode-dependent loss (MDL) estimation technique employing a correction factor to remove the MDL estimation dependence on the SNR when using a minimum mean square error (MMSE) equalizer. A reduction of the MDL estimation error is observed for both transmitterside and in-span MDL emulation.

We propose a neural network model for MDG and optical SNR estimation in SDM transmission. We show that the proposed neural-network-based solution estimates MDG and SNR with high accuracy and low complexity from features extracted after DSP.

In this work the influence of the wavelength variation of a pulsed laser on the size distribution of gold nanoparticles, synthesized by laser ablation in liquid medium, was determined, since this technique is the most used due to its simplicity as it is not developed under strict conditions of temperature, pressure, agitation, etc. Three different wavelengths were used: (i) 532 nm, (ii) 355 nm and (iii) 266 nm, due to equipment restrictions. The distributions and sizes of nanoparticles were studied by dynamic light scattering, scanning electron microscopy and atomic force microscopy. In addition, it is shown that the spectra of the gold region determine the chemical state of the nanoparticle. The results showed that the smallest particle size was obtained with the wavelength of 266 nm with a size of 9.6 nm, while for the other wavelengths; sizes of 55.4 nm and 77.3 nm were obtained respectively. Thus, smallest average diameter of gold nanoparticles was obtained with smallest wavelength.

Space-division multiplexing (SDM) is currently the only solution to cope with the exponential growth of data traffic in optical transmission networks. The performance of long-haul SDM transmission is fundamentally limited by mode-dependent loss (MDL) and mode-dependent gain (MDG) generated in components and amplifiers. To enable the study of MDL/MDG effects in SDM systems as well as MDL/MDG estimation methods within the context of experimental setups, we evaluate an MDL/MDG emulator based on variable optical attenuators (VOAs) and photonic lanterns. We assess MDL/MDG emulation in different attenuation scenarios and demonstrate the capability of the emulator to artificially introduce a wide range of MDL/MDG in a short-reach 3-mode transmission system.

No abstract

We investigate the MIMO equalizer complexity reduction by selective filter deactivation in a 25.9 km 55-mode SDM transmission system. We demonstrate a 21.5% equalizer complexity reduction at the cost of a 4.9% decrease in data rate.