58 citations · 99 across the 14 of their papers we have counts for
14 papers
Quantization of Neural Network Equalizers in Optical Fiber Transmission Experiments
Jamal Darweesh, Nelson Costa, Antonio Napoli +3
The quantization of neural networks for the mitigation of the nonlinear and components' distortions in dual-polarization optical fiber transmission is studied. Two low-complexity n…
Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation
Mohannad Abu-Romoh, Nelson Costa, Yves Jaouën +4
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. LDB…
Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems
Sasipim Srivallapanondh, Pedro J. Freire, Ashraful Alam +6
For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to…
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…
Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays
Pedro J. Freire, Sasipim Srivallapanondh, Michael Anderson +9
In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical t…
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation
Pedro J. Freire, Antonio Napoli, Diego Arguello Ron +7
In this paper, a new methodology is proposed that allows for the low-complexity development of neural network (NN) based equalizers for the mitigation of impairments in high-speed…
Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems
Pedro J. Freire, Michael Anderson, Bernhard Spinnler +8
For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity compensation are implemented in an FPGA, with a level of complexity comparable to tha…
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…
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…
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…
Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls
Pedro J. Freire, Antonio Napoli, Bernhard Spinnler +3
This paper performs a detailed, multi-faceted analysis of key challenges and common design caveats related to the development of efficient neural networks (NN) nonlinear channel eq…
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…
Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems
Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky +4
In this work, we address the question of the adaptability of artificial neural networks (NNs) used for impairments mitigation in optical transmission systems. We demonstrate that b…
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…