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. The approach renders up to 99\% training process reduction, which we demonstrate in three experimental setups.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationSimilar Papers 제목 키워드 기반
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. A "single" NN-based equalizer improves Q-factor by up to 4 dB compared to CDC, without re-trainin…
Multi-Task LearningNeural 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 communicati…
Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems
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 by using well-developed techniques based …
Transfer LearningTowards 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. We demonstr…
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
We propose a novel frequency-domain blind equalization scheme for coherent optical communications. The method is shown to achieve similar performance to its recently proposed time-domain counterpart with lower computatio…