most citedFew-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment

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eess.SP20221 cited

Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection

Sasipim Srivallapanondh, Pedro J. Freire, Bernhard Spinnler +4

To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structur…

eess.SP20221 cited

Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission

Mohannad Abu-romoh, Nelson Costa, Antonio Napoli +3

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…

eess.SP20222 cited

Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment

Jamal Darweesh, Nelson Costa, Antonio Napoli +4

A neural network is quantized for the mitigation of nonlinear and components distortions in a 16-QAM 9x50km dual-polarization fiber transmission experiment. Post-training additive…

eess.SP20221 cited

Learned Digital Back-Propagation for Dual-Polarization Dispersion Managed Systems

Mohannad Abu-romoh, Nelson Costa, Antonio Napoli +3

Digital back-propagation (DBP) and learned DBP (LDBP) are proposed for nonlinearity mitigation in WDM dual-polarization dispersion-managed systems. LDBP achieves Q-factor improveme…

eess.SP2022

Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems

Pedro J. Freire, Bernhard Spinnler, Daniel Abode +7

We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up…

eess.SP2021

Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications

Pedro J. Freire, Yevhenii Osadchuk, Antonio Napoli +5

Addressing the neural network-based optical channel equalizers, we quantify the trade-off between their performance and complexity by carrying out the comparative analysis of sever…