No abstract

No abstract

No abstract

No abstract

A deep learning (DL) based digital backpropagation (DBP) method with a 1 dB SNR gain over a conventional 1 step per span DBP is demonstrated in a 32 GBd 16QAM transmission across 1200 km. The new DL-DPB is shown to require 6 times less computational power over the conventional DBP scheme. The achievement is possible due to a novel training method in which the DL-DBP is blind to timing error, state of polarization rotation, frequency offset and phase offset. An analysis of the underlying mechanism is given. The applied method first undoes the dispersion, compensates for nonlinear effects in a distributed fashion and reduces the out of band nonlinear modulation due to compensation of the nonlinearities by having a low pass characteristic. We also show that it is sufficient to update the elements of the DL network using a signal with high nonlinearity when dispersion or nonlinearity conditions changes. Lastly, simulation results indicate that the proposed scheme is suitable to deal with impairments from transmission over longer distances.

Abstract-The Multicanonical Monte Carlo (MMC) techniqueis a new form of adaptive importance sampling (IS). Thanks to its blind adaptation algorithm, it does not require an in-depth system knowledge for exploitation as does traditional IS. Hence MMC is a practical, handy tool to estimate via simulation the probability of rare events in complex telecom systems, such as the symbol error rate or the outage probability. In this paper, we present the analytical connections between MMC and IS, and describe the recursive algorithm via which MMC seeks an optimal "flat-histogram" warping. We also provide practical guidelines on how MMC can be successfully applied in telecom to achieve accelerations of simulation time by many orders of magnitude with respect to standard Monte Carlo.

No abstract

No abstract