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Found 14 papers, 0 papers with code
Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems
For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems.
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.
Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays
The main results are divided into three parts: a performance comparison, an analysis of how activation functions are implemented, and a report on the complexity of the hardware.
Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection
To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structure.
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation
In this work, we propose and evaluate a Bayesian optimization-assisted compression, in which the hyperparameters of the compression are chosen to simultaneously reduce complexity and improve performance.
Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems
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 that of a dispersion equalizer.
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.
Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems
We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data.
Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls
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 equalizers in coherent optical communication systems.
Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications
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 several neural network architectures, presenting the results for TWC and SSMF set-ups.
Experimental Study of Deep Neural Network Equalizers Performance in Optical Links
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 of TWC fiber.
Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems
We evaluate the capability of transfer learning to adapt the NN to changes in the launch power, modulation format, symbol rate, or even fiber plants (different fiber types and lengths).
Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems
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 equalization in coherent optical communication systems.
