{"title":"ANN/Random forest based performance monitoring in high-speed short-reach optical interconnections","authors":"Jian Zhao , Zihao Su , Yuqing Yang , Tianhua Xu","doi":"10.1016/j.yofte.2024.103941","DOIUrl":null,"url":null,"abstract":"<div><p>In this work, we have developed signal quality monitoring approaches in 100/400 Gbit/s short-reach transmission systems, with the application of four advanced modulation formats. In 100G and 400G transmission systems, it is shown that accuracies of 100 % have been achieved in the modulation format identification (MFI), with the use of random forest (RF) and multitask learning-based artificial neural network (MTL-ANN) for the four modulation formats mentioned. Meanwhile, average mean-square errors (MSEs) of the monitored optical signal-to-noise ratio (OSNRs) are less than 0.1 dB. Random forest uses up to 29 adders and 190 comparators, reducing its complexity by two orders of magnitude compared to MTL-ANN.</p></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"87 ","pages":"Article 103941"},"PeriodicalIF":2.6000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Fiber Technology","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1068520024002864","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, we have developed signal quality monitoring approaches in 100/400 Gbit/s short-reach transmission systems, with the application of four advanced modulation formats. In 100G and 400G transmission systems, it is shown that accuracies of 100 % have been achieved in the modulation format identification (MFI), with the use of random forest (RF) and multitask learning-based artificial neural network (MTL-ANN) for the four modulation formats mentioned. Meanwhile, average mean-square errors (MSEs) of the monitored optical signal-to-noise ratio (OSNRs) are less than 0.1 dB. Random forest uses up to 29 adders and 190 comparators, reducing its complexity by two orders of magnitude compared to MTL-ANN.
期刊介绍:
Innovations in optical fiber technology are revolutionizing world communications. Newly developed fiber amplifiers allow for direct transmission of high-speed signals over transcontinental distances without the need for electronic regeneration. Optical fibers find new applications in data processing. The impact of fiber materials, devices, and systems on communications in the coming decades will create an abundance of primary literature and the need for up-to-date reviews.
Optical Fiber Technology: Materials, Devices, and Systems is a new cutting-edge journal designed to fill a need in this rapidly evolving field for speedy publication of regular length papers. Both theoretical and experimental papers on fiber materials, devices, and system performance evaluation and measurements are eligible, with emphasis on practical applications.