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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.
Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizers
To reduce the complexity of the hardware implementation of neural network-based optical channel equalizers, we demonstrate that the performance of the biLSTM equalizer with approximated activation functions is close to that of the original model.
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.
Computational Complexity Evaluation of Neural Network Applications in Signal Processing
We provide and link four software-to-hardware complexity measures, defining how the different complexity metrics relate to the layers' hyper-parameters.
