Machine-Learning-Aided Optical Fiber Communication System

Pan, Xiaolong; Wang, Xishuo; Tian, Bo; Guizani, Mohsen

doi:10.1109/mnet.011.2000676

Cited by 25 publications

(7 citation statements)

References 13 publications

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“…This is a huge challenge for the fiber-optic backbone network that carries the main traffic of the Internet. Therefore, as the infrastructure of the global communication network, optical fiber communication systems and networks are also faced with the requirements of large capacity and high speed [2]. While commercial products with constellation diagram shaping and powerful forward error correction technology are being built and elastic optical networks are proposed, it also aimed at building a high-speed optical fiber communication system and network with long distance and large capacity, high elasticity, high reliability, and low delay [3].…”

Section: Introductionmentioning

confidence: 99%

Modulation of Multichannel Electronic Communication Signal Parameters Based on a Nonlinear Phase Principle

Zhao

2022

Wireless Communications and Mobile Computing

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In order to solve the difficulty of the optical fiber communication system and network as the infrastructure of the global communication network, which is faced with the requirement of large capacity and high speed, the author proposes a method to modulate the parameters of multichannel electronic communication signals based on the nonlinear phase principle. The method includes multichannel electronic communication signal search, multichannel electronic communication signal parameter modulation, and multichannel electronic communication signal automatic acquisition algorithm realization. Experimental results show that under the conditions that the sampling frequency is set to f = 8.145 MHz, the length of the C/A code period is set to 1 ms, and the 1 ms data packet contains 8145 sampling points, under the same experimental conditions, the capture probability of this experiment reaches 100% and the traditional algorithm has a capture probability of up to 70%. Modulation of multichannel electronic communication signal parameters based on nonlinear phase principle has a positive guiding role in building a high-speed optical fiber communication system and network with long distance, large capacity, high elasticity, high reliability, and low delay.

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Section: Introductionmentioning

confidence: 99%

Modulation of Multichannel Electronic Communication Signal Parameters Based on a Nonlinear Phase Principle

Zhao

2022

Wireless Communications and Mobile Computing

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“…Moreover, these models rely on a number of parameters that may not be available to a network operator. In [15,16] , the authors claim that ML technology will become an important means to providing indispensable technical support to further increase communication capacity and improve future communication stability by effectively identifying physical impairments in the network [17]. In the new generation passive optical networks (PONs) [18], ML-based algorithms have also been successfully applied, particularly to the load-balancing problem [19].…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks

et al. 2023

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This paper examines applying machine learning to the assessment of the quality of the transmission in optical networks. The motivation for research into this problem derives from the fact that the accurate assessment of transmission quality is key to an effective management of an optical network by a network operator. In order to facilitate a potential implementation of the proposed solution by a network operator, the training data for the machine learning algorithms are directly extracted from an operating network via a control plane. Particularly, this work focuses on the application of single class and binary classification machine learning algorithms to optical network transmission quality assessment. The results obtained show that the best performance can be achieved using gradient boosting and random forest algorithms.

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“…Xu et al [59] executed tests for the CIFAR-10, CIFAR-100, and SIT datasets using both network in the network (NIN) and residual network architectures to test the effectiveness of S3Pool in comparison to other pooling techniques (ResNet). According to the experimental observations, S3Pool showed better performance than NIN and ResNet with dropout and stochastic pooling, even when flipping and cropping were used as data augmentation techniques during the testing phase.…”

Section: S3poolingmentioning

confidence: 99%

A Comparison of Pooling Methods for Convolutional Neural Networks

et al. 2022

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One of the most promising techniques used in various sciences is deep neural networks (DNNs). A special type of DNN called a convolutional neural network (CNN) consists of several convolutional layers, each preceded by an activation function and a pooling layer. The feature map of the previous layer is sampled by the pooling layer (that seems to be an important layer) to create a new feature map with condensed resolution. This layer significantly reduces the spatial dimension of the input. It always accomplished two main goals. As a first step, it reduces the number of parameters or weights to minimize computational costs. The second step is to prevent the overfitting of the network. In addition, pooling techniques can significantly reduce model training time and computational costs. This paper provides a critical understanding of traditional and modern pooling techniques and highlights the strengths and weaknesses for readers. Moreover, the performance of pooling techniques on different datasets is qualitatively evaluated and reviewed. This study is expected to contribute to a comprehensive understanding of the importance of CNNs and pooling techniques in computer vision challenges.

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Machine-Learning-Aided Optical Fiber Communication System

Cited by 25 publications

References 13 publications

Modulation of Multichannel Electronic Communication Signal Parameters Based on a Nonlinear Phase Principle

Modulation of Multichannel Electronic Communication Signal Parameters Based on a Nonlinear Phase Principle

A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks

A Comparison of Pooling Methods for Convolutional Neural Networks

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