Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems [PDF]
IEEE Photonics Journal, 2023 In this paper, a joint OSNR and nonlinear noise power estimation scheme based on multi-task deep neural network (MT-DNN) is proposed with the advantages of dispersion-insensitive, modulation-format-transparent for high-speed, long-haul, multi-channel ...
Mengyan Li +6 more
doaj +2 more sources
A Joint OSNR and Nonlinear Distortions Estimation Method for Optical Fiber Transmission System [PDF]
IEEE Photonics Journal, 2018 In this paper, we propose and demonstrate a joint optical signal-to-noise ratio (OSNR) and a nonlinear distortions estimation method. The OSNR is calculated by considering either the link parameters or the correlation operations of the training symbols ...
Yating Xiang +6 more
doaj +2 more sources
A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation [PDF]
Applied Sciences, 2020 In this work, we propose a combined method to implement both modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation, a method based on density-based spatial clustering of applications with a noise (DBSCAN) algorithm ...
Zhao Zhao +3 more
doaj +2 more sources
Joint Modulation Format Identification and OSNR Monitoring Using Cascaded Neural Network With Transfer Learning [PDF]
IEEE Photonics Journal, 2021 We propose a cascaded neural network (NN) to simultaneously identify the modulation formats and monitor the optical-signal-to-noise ratio (OSNR). In the second-level network, it is a single deep NN (DNN) rather than multiple sub-networks, which makes the
Jing Zhang +6 more
semanticscholar +2 more sources
Optics Express, 2019 We experimentally demonstrate a modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring method for digital coherent receivers by using the specific features of received signals' density distributions in Stokes axes ...
A. Yi +6 more
semanticscholar +2 more sources
A Robust Reference Optical Spectrum Based in-Band OSNR Monitoring Method Suitable for Flexible Optical Networks [PDF]
IEEE Photonics Journal, 2020 The in-band OSNR monitoring methods based on the noise-free reference optical spectrum (ROS) have attracted much attention in the field of optical performance monitoring because they are cost-effective and insensitive to chromatic dispersion and ...
Yibo Zhong +6 more
semanticscholar +2 more sources
Simultaneous multi-channel OSNR monitoring with a wavelength selective switch
Optics Express, 2010 We show the first simultaneous OSNR monitoring of two 40 Gb/s OOK and DPSK channels, using only a wavelength selective switch and two slow photodetectors. Our approach is modulation format and bit-rate independent and can easily be included in existing reconfigurable networks.
Jochen, Schröder +5 more
openaire +3 more sources
Reference Optical Spectrum Based In-Band OSNR Monitoring Method for EDFA Amplified Multispan Optical Fiber Transmission System With Cascaded Filtering Effect [PDF]
IEEE Photonics Journal, 2018 The reference optical spectrum (ROS) based in-band optical signal-to-noise-ratio (OSNR) monitoring methods are accurate and inherently robust to fiber chromatic dispersion and polarization mode dispersion.
Guo Yin +3 more
doaj +2 more sources
IEEE Photonics Journal, 2018 Brillouin optical spectrum analyzer (BOSA) based on stimulated Brillouin scattering (SBS) can provide ultra-high resolution, which in theory can greatly improve the optical signal-to-noise ratio (OSNR) measurement accuracy, especially for systems with ...
Yibo Zhong +7 more
doaj +2 more sources
Statistical moments-based OSNR monitoring for coherent optical systems
Optics Express, 2012 We present a novel statistical moments-based method for optical signal-to-noise ratio (OSNR) monitoring in polarization-multiplexed (pol-mux) coherent optical systems. This technique only requires the knowledge of the envelope of the equalized signal before phase correction, which can be achieved by using any two arbitrary statistical moments, and it ...
Chen, Zhu +7 more
openaire +3 more sources