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Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects. In this work, a machine learning-based classification technique, known as the Parzen window (PW) classifier, is applied to mitigate the nonlinear effects in the optical channel. The PW classifier is used as a detector with improved nonlinear decision boundaries more adapted to the nonlinear fiber channel. Performance improvement is observed when applying the PW in the context of dispersion managed and dispersion unmanaged systems.
We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection
Fiber Kerr nonlinearity is a fundamental limitation to the achievable capacity of long-distance optical fiber communication. Digital back-propagation (DBP) is a primary methodology to mitigate both linear and nonlinear impairments by solving the inve
We present a novel end-to-end autoencoder-based learning for coherent optical communications using a parallelizable perturbative channel model. We jointly optimized constellation shaping and nonlinear pre-emphasis achieving mutual information gain of
We report on the observation of dispersion-managed (DM) dark soliton emission in a net-normal dispersion erbium-doped fiber laser. We found experimentally that dispersion management could not only reduce the pump threshold for the dark soliton format
Current optical coherent transponders technology is driving data rates towards 1 Tb/s/{lambda}and beyond. This trend requires both high-performance coded modulation schemes and efficient implementation of the forward-error-correction (FEC) decoder. A