{"title":"Identification of advanced optical modulation formatand estimation of signal-to-noise-ratio based on parallel-twin convolutional neural network","authors":"Xiaowei Dong, Zhihui Yu","doi":"10.37190/oa230209","DOIUrl":null,"url":null,"abstract":"In this paper, we design a parallel-twin convolutional neural network (PT-CNN) deep learning model and use the signal constellation diagram to realize the identification of six advanced optical modulation formats (QPSK, 4QAM, 8PSK, 8QAM, 16PSK, 16QAM) and signal-to-noise-ratio (SNR) estimation. The influence of PT-CNN with different layers and kernel sizes is investigated and the optimal network model is chosen. Simulation results demonstrate that the proposed method has the advantages of not requiring manual feature extraction, having the ability to clearly distinguish the six modulation formats with 100% accuracy when SNR of the received signal sequences is higher than 12 dB. In addition, the high-accurate SNR estimation is realized simultaneously without increasing additional system complexity.","PeriodicalId":19589,"journal":{"name":"Optica Applicata","volume":"1 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optica Applicata","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.37190/oa230209","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we design a parallel-twin convolutional neural network (PT-CNN) deep learning model and use the signal constellation diagram to realize the identification of six advanced optical modulation formats (QPSK, 4QAM, 8PSK, 8QAM, 16PSK, 16QAM) and signal-to-noise-ratio (SNR) estimation. The influence of PT-CNN with different layers and kernel sizes is investigated and the optimal network model is chosen. Simulation results demonstrate that the proposed method has the advantages of not requiring manual feature extraction, having the ability to clearly distinguish the six modulation formats with 100% accuracy when SNR of the received signal sequences is higher than 12 dB. In addition, the high-accurate SNR estimation is realized simultaneously without increasing additional system complexity.
期刊介绍：
Acoustooptics, atmospheric and ocean optics, atomic and molecular optics, coherence and statistical optics, biooptics, colorimetry, diffraction and gratings, ellipsometry and polarimetry, fiber optics and optical communication, Fourier optics, holography, integrated optics, lasers and their applications, light detectors, light and electron beams, light sources, liquid crystals, medical optics, metamaterials, microoptics, nonlinear optics, optical and electron microscopy, optical computing, optical design and fabrication, optical imaging, optical instrumentation, optical materials, optical measurements, optical modulation, optical properties of solids and thin films, optical sensing, optical systems and their elements, optical trapping, optometry, photoelasticity, photonic crystals, photonic crystal fibers, photonic devices, physical optics, quantum optics, slow and fast light, spectroscopy, storage and processing of optical information, ultrafast optics.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
