Applied constant gain amplification in circulating loop experiments

The reconfiguration of channel or wavelength routes in optically transparent mesh networks can lead to deviations in channel power that may impact transmission performance. A new experimental approach, applied constant gain, is used to maintain constant gain in a circulating loop enabling the study of gain error effects on long-haul transmission under reconfigured channel loading. Using this technique we examine a number of channel configurations and system tuning operations for both full-span dispersion-compensated and optimized dispersion-managed systems. For each system design, large power divergence was observed with a maximum of 15 dB at 2240 km, when switching was implemented without additional system tuning. For a bit error rate of 10-3, the maximum number of loop circulations was reduced by up to 33%

Dynamic circulating-loop methods for transmission experiments in optically transparent networks

Recent experiments incorporating multiple fast switching elements and automated system configuration in a circulating loop apparatus have enabled the study of aspects of long-haul WDM transmission unique to optically transparent networks. Techniques include per-span switching to measure the performance limits due to dispersion compensation granularity and mesh network walk-off, and applied constant-gain amplification to evaluate wavelength reconfiguration penalties

Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks

We experimentally solve the problem of maximizing capacity under a total supply power constraint in a massively parallel submarine cable context, i.e., for a spatially uncoupled system in which fiber Kerr nonlinearity is not a dominant limitation. By using multi-layer neural networks trained with extensive measurement data acquired from a 12-span 744-km optical fiber link as an accurate digital twin of the true optical system, we experimentally maximize fiber capacity with respect to the transmit signal's spectral power distribution based on a gradient-descent algorithm. By observing convergence to approximately the same maximum capacity and power distribution for almost arbitrary initial conditions, we conjecture that the capacity surface is a concave function of the transmit signal power distribution. We then demonstrate that eliminating gain flattening filters (GFFs) from the optical amplifiers results in substantial capacity gains per Watt of electrical supply power compared to a conventional system that contains GFFs.Comment: arXiv admin note: text overlap with arXiv:1910.0205

Application of mass spectrometry in bacterial metabolomics

NRC publication: Ye

Maximizing Fiber Cable Capacity Under A Supply Power Constraint Using Deep Neural Networks

We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using deep neural networks in a parallel fiber context. (C) 2020 The Author