Deep Learning Waveform Modeling for Wideband Optical Fiber Channel
Abstract Fast and accurate optical fiber communication simulation systems are crucial for optimizing optical networks, developing digital signal processing algorithms, and performing end-to-end (E2E)
Machine learning-based models for optical fiber channels
The former treats channel modeling as a "black box" providing rapid modeling capabilities at the expense of transparency and substantial data requirements. In contrast, the latter integrate physical
Channel capacity and modeling of optical fiber
We consider the communication channel given by a fiber optical transmission line. We develop a method to perturbatively calculate the
Fiber channel modeling based on CGAN and three
The CGAN is employed for fiber channel modeling, and the autoencoder realizes 3D geometric shaping of carrierless amplitude phase (CAP)-16, 32, and 64, which achieves an
Fiber Channel Modeling for Coherent Optical Fiber Communication
Optical fiber channel modeling plays a vital role in the simulation, design, and performance assessment of optical fiber communication systems. Here, a new deep learning architecture, the
A Practical channel modeling method for few-mode optical fiber
A channel modeling method is developed and proposed to practically model the various effects of mode couplings for optical communication systems with multi-core fiber (MCF) and/or few-mode fiber
Machine learning-based models for optical fiber channels
Opticalfiber communication particularly in channel modeling.Itdiscussestheevolutionfrom Channelmodeling Machine learning conventional methods to ML-based approaches that aim to
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.
Optical Fiber Channel Modeling Using Conditional Generative
In optical fiber communications, the model of fiber channel is significant for system simulation and research, but it is always time-consuming for the complex mathematic calculations and needs expert
Fiber channel modeling based on CGAN and three
To optimize the complex nonlinear effects in optical communication systems, this paper introduces channel modeling and three-dimensional (3D) geometric shaping based on end-to-end
Machine learning-based models for optical fiber channels
This paper presents a comprehensive review of machine learning (ML) in optical fiber communications, particularly in channel modeling. It discusses the evolution from conventional
A fiber channel modeling method based on complex neural networks
Channel modeling plays a pivotal role in the field of communications, particularly in the optical communication networks of backbone communication systems. Recent studies on optical channel
Fast and Accurate Optical Fiber Channel Modeling using Generative
In this paper, we employ the GAN to model the optical fiber channel with the characteristics of chromatic dispersion (CD), self-phase modulation (SPM), attenuation, and amplified spontaneous emission
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
Deep Learning Waveform Channel Modeling for Wideband Optical Fiber
Abstract—Fast and accurate waveform simulation is critical for characterizing optical fiber channel behavior, developing digital signal processing (DSP) algorithms, optimizing optical network
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
Deep Learning Waveform Channel Modeling for Wideband Optical
Fast and accurate waveform simulation is critical for understanding fiber channel characteristics, developing digital signal processing (DSP) technologies, optimizing optical network configurations,
A fiber channel modeling method based on complex neural networks
The proposed model can adequately meet the precision requirements for optical communication system modeling while maintaining low complexity. The organization of this article is
Fast and Accurate Optical Fiber Channel Modeling using
Abstract—In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function. Our
Data-driven Optical Fiber Channel Modeling Using Fourier Neural
We utilize Fourier neural operator to accurately model a 1200km optical fiber channel. It can achieve similar performance compared with SSFM, while with lower computational complexity (the running
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