An efficient specific emitter identification method based on complex-valued neural networks and network compression
Specific emitter identification (SEI) is a promising technology to discriminate the individual
emitter and enhance the security of various wireless communication systems. SEI is …
emitter and enhance the security of various wireless communication systems. SEI is …
Performance versus complexity study of neural network equalizers in coherent optical systems
We present the results of the comparative performance-versus-complexity analysis for the
several types of artificial neural networks (NNs) used for nonlinear channel equalization in …
several types of artificial neural networks (NNs) used for nonlinear channel equalization in …
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 …
used for impairments mitigation in optical transmission systems. We demonstrate that by …
Optical phase conjugation with complex-valued deep neural network for WDM 64-QAM coherent optical systems
We experimentally demonstrated a photoelectric nonlinear compensation scheme of optical
phase conjugation (OPC) with complex-valued deep neural network (CVDNN) to mitigate …
phase conjugation (OPC) with complex-valued deep neural network (CVDNN) to mitigate …
Bayesian optimization for nonlinear system identification and pre-distortion in cognitive transmitters
M Sena, MS Erkilinc, T Dippon, B Shariati… - Journal of Lightwave …, 2021 - opg.optica.org
We present a digital signal processing (DSP) scheme that performs hyperparameter tuning
(HT) via Bayesian optimization (BO) to autonomously optimize memory tap distribution of …
(HT) via Bayesian optimization (BO) to autonomously optimize memory tap distribution of …
Experimental study of deep neural network equalizers performance in optical links
PJ Freire, Y Osadchuk, B Spinnler… - 2021 Optical Fiber …, 2021 - ieeexplore.ieee.org
We propose a convolutional-recurrent channel equalizer and experimentally demonstrate
1dB Q-factor improvement both in single-channel and 96× WDM, DP-16QAM transmission …
1dB Q-factor improvement both in single-channel and 96× WDM, DP-16QAM transmission …
Direct decoding of nonlinear OFDM-QAM signals using convolutional neural network
Nonlinear Fourier transform, as a technique that has a great potential to overcome the
capacity limit in fibre optical communication system, faces speed and accuracy bottlenecks …
capacity limit in fibre optical communication system, faces speed and accuracy bottlenecks …
[HTML][HTML] Neural networks for computing and denoising the continuous nonlinear Fourier spectrum in focusing nonlinear Schrödinger equation
We combine the nonlinear Fourier transform (NFT) signal processing with machine learning
methods for solving the direct spectral problem associated with the nonlinear Schrödinger …
methods for solving the direct spectral problem associated with the nonlinear Schrödinger …
Deep neural network-aided soft-demapping in optical coherent systems: Regression versus classification
We examine here what type of predictive modelling, classification, or regression, using
neural networks (NN), fits better the task of soft-demapping based post-processing in …
neural networks (NN), fits better the task of soft-demapping based post-processing in …
Application of complex fully connected neural networks to compensate for nonlinearity in fibre-optic communication lines with polarisation division multiplexing
A scheme is proposed to compensate for nonlinear distortions in extended fibre-optic
communication lines with polarisation division multiplexing, based on fully connected neural …
communication lines with polarisation division multiplexing, based on fully connected neural …