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Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation
In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links.
Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission
A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3. 5 dB improvement in MSE compared to DBP with comparable complexity.
Complexity Reduction over Bi-RNN-Based Nonlinearity Mitigation in Dual-Pol Fiber-Optic Communications via a CRNN-Based Approach
Bidirectional recurrent neural networks (bi-RNNs), in particular, bidirectional long short term memory (bi-LSTM), bidirectional gated recurrent unit, and convolutional bi-LSTM models have recently attracted attention for nonlinearity mitigation in fiber-optic communication.
Learned Digital Back-Propagation for Dual-Polarization Dispersion Managed Systems
Digital back-propagation (DBP) and learned DBP (LDBP) are proposed for nonlinearity mitigation in WDM dual-polarization dispersion-managed systems.
Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment
A neural network is quantized for the mitigation of nonlinear and components distortions in a 16-QAM 9x50km dual-polarization fiber transmission experiment.
Approximating Probability Distributions by ReLU Networks
We also obtain a lower bound on the minimum number of neurons needed to approximate the histogram distributions.
