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

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

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 Study of Deep Neural Network Equalizers Performance in Optical Links

Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler +5

We propose a convolutional-recurrent channel equalizer and experimentally demonstrate 1dB Q-factor improvement both in single-channel and 96 x WDM, DP-16QAM transmission over 450km…

eess.SP2021

Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler +5

We present the results of the comparative analysis of the performance versus complexity for several types of artificial neural networks (NNs) used for nonlinear channel equalizatio…