Farman Ali , Haleem Afsar , Ali Alshamrani , Ammar Armghan
{"title":"基于机器学习的光通信电网热损伤和非线性损伤缓解技术","authors":"Farman Ali , Haleem Afsar , Ali Alshamrani , Ammar Armghan","doi":"10.1016/j.optlastec.2024.112090","DOIUrl":null,"url":null,"abstract":"<div><div>Nonlinear impairments (NIs) act as limiting factors in the performance of long-haul optical communication grids (OCGs), particularly when operating at 100 Gbps over many channels. These deficiencies become worse by the thermal optics effect, which alter the refractive index of optical components and medium leading to signal degradation . This paper introduces a machine learning (ML)-enhanced technique that uses a convolutional neural network (CNN) to reduce distortions induced by NIs while taking thermal dynamics into account. We expand our investigation to evaluate the quality of service of OCGs under the dual impact of NIs and thermal variations, employing advanced modulation schemes such as polarization division multiplexing 64 quadrature amplitude modulation (PDM-64QAM) and dual-polarization quadrature phase-shift keying (DP-QPSK). Extensive simulations, using a split-step Fourier (SSF) method, are performed to model the combined effects of NIs and thermal dynamics on optical signals. Our methodology is supported by stochastic analysis, which simulates the network’s performance while focusing on activation functions that account for thermal impacts on NIs. Our results show that the CNN-based method, in conjunction with advanced modulation schemes, significantly reduces bit error rate (BER) and improves signal-to-noise ratio (SNR), outperforming traditional methods such as support vector machines (SVM) and digital backpropagation (DBP). The proposed approach demonstrates the potential to enhance the quality of transmission (QoT) in OCGs, making it a viable solution for future high-capacity, thermally influenced optical networks.</div></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"182 ","pages":"Article 112090"},"PeriodicalIF":4.6000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-based mitigation of thermal and nonlinear impairments in optical communication grids\",\"authors\":\"Farman Ali , Haleem Afsar , Ali Alshamrani , Ammar Armghan\",\"doi\":\"10.1016/j.optlastec.2024.112090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Nonlinear impairments (NIs) act as limiting factors in the performance of long-haul optical communication grids (OCGs), particularly when operating at 100 Gbps over many channels. These deficiencies become worse by the thermal optics effect, which alter the refractive index of optical components and medium leading to signal degradation . This paper introduces a machine learning (ML)-enhanced technique that uses a convolutional neural network (CNN) to reduce distortions induced by NIs while taking thermal dynamics into account. We expand our investigation to evaluate the quality of service of OCGs under the dual impact of NIs and thermal variations, employing advanced modulation schemes such as polarization division multiplexing 64 quadrature amplitude modulation (PDM-64QAM) and dual-polarization quadrature phase-shift keying (DP-QPSK). Extensive simulations, using a split-step Fourier (SSF) method, are performed to model the combined effects of NIs and thermal dynamics on optical signals. Our methodology is supported by stochastic analysis, which simulates the network’s performance while focusing on activation functions that account for thermal impacts on NIs. Our results show that the CNN-based method, in conjunction with advanced modulation schemes, significantly reduces bit error rate (BER) and improves signal-to-noise ratio (SNR), outperforming traditional methods such as support vector machines (SVM) and digital backpropagation (DBP). The proposed approach demonstrates the potential to enhance the quality of transmission (QoT) in OCGs, making it a viable solution for future high-capacity, thermally influenced optical networks.</div></div>\",\"PeriodicalId\":19511,\"journal\":{\"name\":\"Optics and Laser Technology\",\"volume\":\"182 \",\"pages\":\"Article 112090\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optics and Laser Technology\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0030399224015482\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics and Laser Technology","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030399224015482","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
Machine learning-based mitigation of thermal and nonlinear impairments in optical communication grids
Nonlinear impairments (NIs) act as limiting factors in the performance of long-haul optical communication grids (OCGs), particularly when operating at 100 Gbps over many channels. These deficiencies become worse by the thermal optics effect, which alter the refractive index of optical components and medium leading to signal degradation . This paper introduces a machine learning (ML)-enhanced technique that uses a convolutional neural network (CNN) to reduce distortions induced by NIs while taking thermal dynamics into account. We expand our investigation to evaluate the quality of service of OCGs under the dual impact of NIs and thermal variations, employing advanced modulation schemes such as polarization division multiplexing 64 quadrature amplitude modulation (PDM-64QAM) and dual-polarization quadrature phase-shift keying (DP-QPSK). Extensive simulations, using a split-step Fourier (SSF) method, are performed to model the combined effects of NIs and thermal dynamics on optical signals. Our methodology is supported by stochastic analysis, which simulates the network’s performance while focusing on activation functions that account for thermal impacts on NIs. Our results show that the CNN-based method, in conjunction with advanced modulation schemes, significantly reduces bit error rate (BER) and improves signal-to-noise ratio (SNR), outperforming traditional methods such as support vector machines (SVM) and digital backpropagation (DBP). The proposed approach demonstrates the potential to enhance the quality of transmission (QoT) in OCGs, making it a viable solution for future high-capacity, thermally influenced optical networks.
期刊介绍:
Optics & Laser Technology aims to provide a vehicle for the publication of a broad range of high quality research and review papers in those fields of scientific and engineering research appertaining to the development and application of the technology of optics and lasers. Papers describing original work in these areas are submitted to rigorous refereeing prior to acceptance for publication.
The scope of Optics & Laser Technology encompasses, but is not restricted to, the following areas:
•development in all types of lasers
•developments in optoelectronic devices and photonics
•developments in new photonics and optical concepts
•developments in conventional optics, optical instruments and components
•techniques of optical metrology, including interferometry and optical fibre sensors
•LIDAR and other non-contact optical measurement techniques, including optical methods in heat and fluid flow
•applications of lasers to materials processing, optical NDT display (including holography) and optical communication
•research and development in the field of laser safety including studies of hazards resulting from the applications of lasers (laser safety, hazards of laser fume)
•developments in optical computing and optical information processing
•developments in new optical materials
•developments in new optical characterization methods and techniques
•developments in quantum optics
•developments in light assisted micro and nanofabrication methods and techniques
•developments in nanophotonics and biophotonics
•developments in imaging processing and systems