Machine learning-based models for optical fiber channels
This review thus serves as a guide for researchers to evaluate, compare, and implement ML-based fiber channel models, and to explore their applications and challenges in increasingly
Deep Learning Waveform Channel Modeling for Wideband Optical
We believe that the standardized evaluation framework, comparative results, and in-depth challenge analysis presented in this work will facilitate the rapid advancement of DL-based channel modeling in
Application scenarios for simultaneous optical p ower and d ata transfer
ABSTRACT ions, utilizing both fiber-coupled systems and free-space optical links. The integration of these technologies into a single link simplifies system design while combining the benefits of
A fiber channel modeling method based on complex neural networks
As the foundation for achieving efficient and reliable data transmission, optical communication channels cover a wide range of applications, from home broadband access to global
Machine learning-based models for optical fiber channels
Nevin et al. provide an introductory overview, discussing key ML meth-ods and their broad applications across optical fiber communications without extensive methodological classifications .
Information-theory-friendly models for fiber-optic channels: A primer
In this tutorial paper, we review several channel models used in optical communications, and discuss their suitability for information-theoretic analyses. Key issues are how nonlinearity, channel memory,
Convolutional Neural Network-Based Fiber Optic
To cover this research gap and further enrich the fiber-longitudinal PPE implementation methods, this paper proposes a fiber channel emulator
A fiber channel modeling method based on complex
To address this limitation, we propose a complex-valued conditional generative adversarial network (C-CGAN) in this paper to comprehensively
CFOSD Case Studies
These are a series of case studies appropriate for the FTTH labs created for use as laboratory and homework exercises as part of classes preparing for the FOA CFOS/D Fiber Optic Network Design
Machine learning-based models for optical fiber channels
Motivated by the concurrent trends of rising system complexity, advancements in ML, and the requirements of channel modeling, a growing number of studies focus on utilizing ML-based
Information-theory-friendly models for fiber-optic channels: A primer
There exists a rich flora of channel models for optical fiber channels, which differ not only in the types of transmission scenario they describe but also in the type of analysis they support. In this tutorial
A contemporary survey on free space optical communication:
Under link budget design, the received optical power can be easily estimated in a fiber optic communication, whereas, it is unpredictable, complex, and inhomogeneous in air because of
A fiber channel modeling method based on complex neural networks
To the best of our knowledge, this is the first application of CVNN to optical channel modeling. In the field of neural network modeling, model complexity represents a critical consideration.
GIW123.doc
Application Scenario and Key Technology Analysis of 5g Communication Technology Based on Optical Fiber Transmission Network Jiahe Peng Information Technology Department, Yunnan College of
Application scenarios for simultaneous optical p ower and d ata transfer
ABSTRACT Optical power transfer and optical data communication are versatile technologies with numerous applications, utilizing both fiber-coupled systems and free-space optical links. The
Fast and Accurate Optical Fiber Channel Modeling using Generative
T HE modeling of optical fiber channel is significant for system designs and simulations. The conventional channel modeling is based on split-step Fourier method (SSFM), which is carried out by
Convolutional Neural Network-Based Fiber Optic
Wang D, Wen K, Bai T, Xia R, Zhao Z, Gao G. Convolutional Neural Network-Based Fiber Optic Channel Emulator and Its Application to Fiber
Performance Assessment of Deep Learning based Channel Modeling
We compare and study three data-driven channel modeling methods based on deep learning in fiber optic communication systems. TTHNet performing the best among th.
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