Abstract
Practical implementation of digital signal processing for mitigation of transmission impairments in optical communication systems requires reduction of the complexity of the underlying algorithms. Here, we investigate the application of convolutional neural networks for compensating nonlinear signal distortions in a 3200 km fiber-optic 11x400-Gb/s WDM PDM-16QAM transmission link with a focus on the optimization of the corresponding algorithmic complexity. We propose a design that includes original initialisation of the weights of the layers by a filter predefined through the training a single-layer convolutional neural network. Furthermore, we use an enhanced activation function that takes into account nonlinear interactions between neighbouring symbols. To increase learning efficiency, we apply a layer-wise training scheme followed by joint optimization of all weights applying additional training to all of them together in the large multi-layer network. We examine application of the proposed convolutional neural network for the nonlinearity compensation using only one sample per symbol and evaluate complexity and performance of the proposed technique.
Highlights
C APACITY demand in communication networks follows a stable increasing trend over the recent decades due to the continuing expansion of current and emerging digital applications and services
A neural network with linear filters width less than 50 coefficients cannot effectively compensate for the chromatic dispersion (CD) and the resulting bit error rate (BER) level is higher than one-step CD compensation in frequency domain
The introduced deep convolutional neural network (DCNN) architectures mimics the traditional digital backward propagation (DBP) algorithm by using each linear convolutional layer to compensate for the chromatic dispersion on a subsection of the link and the nonlinear activation layer to cancel the corresponding Kerr-effect induced nonlinearity
Summary
C APACITY demand in communication networks follows a stable increasing trend over the recent decades due to the continuing expansion of current and emerging digital applications and services. This fact calls for new approaches to improve the transmission performance of optical fiber links. The output signal is given by the solution of a nonlinear stochastic partial differential equation(s) with the input signal defining the initial conditions of the problem It is well understood nowadays, that nonlinear fiber communication channels require the development of conceptually new digital processing methods capable to deal with the nonlinear transmission impairments In this work we develop a new design of a deep convolutional neural network (DCNN) for mitigating the nonlinear signal distortions in a long-haul fiber communication system. We conducted extensive analysis of computational complexity of the equalizer based on deep convolutional neural network and showed the superiority of the proposed scheme over conventional DBP methods.
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