FPGA implementation of multi-layer machine learning equalizer with on-chip training
K Liu, E Börjeson, C Häger… - Optical Fiber …, 2023 - opg.optica.org
We design and implement an adaptive machine learning equalizer that alternates multiple
linear and nonlinear computational layers on an FPGA. On-chip training via gradient …
linear and nonlinear computational layers on an FPGA. On-chip training via gradient …
Precise longitudinal power monitoring over 2,080 km enabled by step size selection of split step Fourier method
T Sasai, M Nakamura, E Yamazaki… - 2022 Optical Fiber …, 2022 - ieeexplore.ieee.org
Precise Longitudinal Power Monitoring over 2,080 km Enabled by Step Size Selection of Split
Step Fourier Method Page 1 Precise Longitudinal Power Monitoring over 2,080 km Enabled …
Step Fourier Method Page 1 Precise Longitudinal Power Monitoring over 2,080 km Enabled …
Fusing physics to fiber nonlinearity model for optical networks based on physics-guided neural networks
Machine learning (ML) has been widely used for physical layer modeling in optical networks
for its high accuracy and efficient calculation structure. However, traditional ML-based …
for its high accuracy and efficient calculation structure. However, traditional ML-based …
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 …
An interpretable mapping from a communication system to a neural network for optimal transceiver-joint equalization
In this paper, we propose a scheme that utilizes the optimization ability of artificial
intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical …
intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical …
Joint pmd tracking and nonlinearity compensation with deep neural networks
Overcoming fiber nonlinearity is one of the core challenges limiting the capacity of optical
fiber communication systems. Machine learning based solutions such as learned digital …
fiber communication systems. Machine learning based solutions such as learned digital …
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 …
equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links …
Improved DBSCAN algorithm based signal recovery technology in coherent optical communication systems
X Huang, Y Wang, C Li, H Xu, Q Zhang, L Yang… - Optics …, 2022 - Elsevier
Signal recovery technology based on an improved density-based spatial clustering of
applications with a noise algorithm is proposed for coherent optical communication systems …
applications with a noise algorithm is proposed for coherent optical communication systems …
Deep learning—a route to WDM high-speed optical networks
S Rai, AK Garg - Journal of Optics, 2024 - Springer
The evolution of Internet and communication systems is exponentially increasing the
complexity in communication networks. This paved the way for the incorporation of artificial …
complexity in communication networks. This paved the way for the incorporation of artificial …