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The material focuses on AI extending into the infrastructure layer by turning real-time sensing and operational data from optical modules, fiber resources, and traffic awareness into end-to-end intelligence. It details intelligent optical module sensing, passive fiber sensing, and service traffic awareness, then presents practical AI fault classification and root-cause discrimination for metro and passive WDM fronthaul, including fault sampling enhancements and lightweight millisecond-granularity power analysis.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/intelligent-devices-networking-of-transport-networks-evolution-and-practice-ai-driven-intelligent-sensing-for-optical-fiber/232788/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/intelligent-devices-networking-of-transport-networks-evolution-and-practice-ai-driven-intelligent-sensing-for-optical-fiber/232788.png","ImageObject",300,407,{"name":92,"@type":93},"Ben Jamin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-09","2026-09-10",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"How does AI extend transport networks into the infrastructure layer?","Question",{"text":112,"@type":113},"Real-time data from components, modules, and fiber infrastructure enables end-to-end intelligence through data collection, intelligent analysis, and coordinated scheduling.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What parameters and capabilities are used for intelligent optical module sensing?",{"text":117,"@type":113},"The document lists basic parameters such as temperature, voltage, and Tx/Rx optical power, plus operational status monitoring and degradation assessment using optical metrics like OSNR, CD, PMD, and polarization state.",{"name":119,"@type":110,"acceptedAnswer":120},"What fault types and enhancements are used in metro-network AI fault classification?",{"text":121,"@type":113},"Five typical fault types are covered: device failure, fiber break, pigtail detachment, fiber bending, and connector loosen. Two key enhancements include sampling enhancement with fault-triggered 10ms-resolution power transient capture and a lightweight AI algorithm for millisecond-granularity diagnostics.",{"name":123,"@type":110,"acceptedAnswer":124},"How does the passive WDM fronthaul approach distinguish fault layers?",{"text":125,"@type":113},"It uses AI multi-modal data fusion for root-cause discrimination with a two-stage process: fault classification and fault localization. Multi-modal inputs (e.g., temperature and optical power) improve discrimination accuracy versus an RNN baseline.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},232788,1789073652,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":133,"read_time":148},2336478466772,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Intelligent Devices & Networking of  \nTransport Networks: Evolution and Practice  \nLiuyan Han, China Mobile Research Institute  \nION-2030: Opening the Era of Intelligent Transport Networks  \nInternational Optical Network for 2030 and beyond  \nDeep convergence of AI with transport networks will reshape networking, evolving Internet of Everything into Intelligence of Everything .  \nPTN/SPN  \nPTN/SPN  \nCPE OTN  \nOLT  \n400G OTN  \n5G/5G-A/6G BS  \nHome  \nEnterprise  \nOpen-WDM  \nAI Computing Center  \nOptical networks have rich resources for AI-driven evolution. Intelligent sensing is where AI gets its data foundation.  \nAI for Network  \nAI  \nAI  \nAI  \nIntelligence Extends to the Infrastructure Layer  \nReal-time data from components, modules, and fiber infrastructure enables end-to-end transport network intelligence  \nacross data collection, intelligent analysis, and coordinated scheduling  \nAI for Network goes beyond the networking level, down to components, modules, and fiber links .  \nThis will form the foundation of an intelligent transport network.  \nContents  \nIntelligent Optical Module Sensing  \nIntelligent Passive Fiber Resource Sensing  \nIntelligent Service Traffic Awareness  \nIntelligent Optical Network Evolution  \nIntelligent Optical Modules : from connection to sensing  \nAs the primary module for sensing and monitoring optical path states via the data collection capability, optical modules become the natural entry point for AI applications .  \nBasic parameters: temperature, voltage, Tx/Rx optical power  \nOperational status monitoring  \n~~ ~~ AI  \nBasis for degradation assessment  \nOptical modules evolve from network connection points to sensing units.  \nAI can analyze and diagnose optical link status.  \nOptical parameters:  \nOSNR, CD, PMD, polarization state, etc  \nBasic link physical information  \n AI  \nBasis for intelligent diagnostics, performance optimization  \nPractice: AI Fault Classification in Metro Network  \nEnabling fast diagnostics for optical path faults at the metro edge, enhancing the reliability of single-homed CPE connections .  \nFive Typical Fault Types  \nDevice failure  \nFiber break  \nPigtail detachment  \nFiber bending  \nConnector loosen  \nTwo Key Enhancements  \n① Sampling enhancement  \nSimilar patterns across different faults at second-level sampling  \nFault Triggered  \nSoftware-triggered capturing optical power transients at 10ms resolution  \n② Lightweight AI algorithm  \nAI-Embedded Optical Modules With Millisecond-Granularity Power Analysis for Autonomous Metro Transport Network and Field Trial, JLT 43(22), 2025  \nFault diagnose time from hours to seconds Precise recognition accuracy of >95%  \nPractice : AI Fault Classification in Passive WDM Fronthaul  \nFaults on the common fiber, branches, and multiplexer/demultiplexer all show similar symptoms of optical power attenuation .  \nTraditional O&M cannot accurately distinguish the fault layer.  \nPoint-to-multipoint architecture:  \nHard to pinpoint fault layer  \nRRU  \nFailure at the channel port side  \nFailure at the common port side  \nBBU  \nAI Multi-modal data fusion for Root-Cause Discrimination  \nStage1： Fault Classification  \nStage2： Fault Localization  \nMulti-modal fusion (temperature, optical power, etc. ) as CNN input  \n92.4% accuracy in branch-vs-common fault discrimination, a 28.8% improvement over the RNN baseline.  \nFirst Demonstration of Autonomous Fault Diagnosis with AI-Embedded Optical Modules in Passive WDM Systems for Front-Haul Network, OFC 2026, M3A.3  \nContents  \nIntelligent Optical Module Sensing  \nIntelligent Passive Fiber Resource Sensing  \nIntelligent Service Traffic Awareness  \nIntelligent Optical Network Evolution  \nAI-Driven Intelligent Sensing for Optical Fiber  \n ① Distributed Optical Fiber Sensing Based on Backscattering  \n② Forward Sensing Based on Coherent oDSP  \noDSP LS OFDR OTDR OPM DAS/DTS/DPS oDSP LS  \nSOP OTDR Curve Optical Power Real-time Environmental ODSP  \nOptical fiber sensing data are characterized by high diversi","cbCaisL1UFRd7Lww","https://ap.wps.com/l/cbCaisL1UFRd7Lww","pdf",4002636,21,"English","# ION-2030: Opening the Era of Intelligent Transport Networks\n## Intelligence Extends to the Infrastructure Layer\n## Contents\n### Intelligent Optical Module Sensing\n### Intelligent Passive Fiber Resource Sensing\n### Intelligent Service Traffic Awareness\n### Intelligent Optical Network Evolution\n## Practice: AI Fault Classification in Metro Network\n## Practice: AI Fault Classification in Passive WDM Fronthaul\n## AI-Driven Intelligent Sensing for Optical Fiber","[{\"question\":\"How does AI extend transport networks into the infrastructure layer?\",\"answer\":\"Real-time data from components, modules, and fiber infrastructure enables end-to-end intelligence through data collection, intelligent analysis, and coordinated scheduling.\"},{\"question\":\"What parameters and capabilities are used for intelligent optical module sensing?\",\"answer\":\"The document lists basic parameters such as temperature, voltage, and Tx/Rx optical power, plus operational status monitoring and degradation assessment using optical metrics like OSNR, CD, PMD, and polarization state.\"},{\"question\":\"What fault types and enhancements are used in metro-network AI fault classification?\",\"answer\":\"Five typical fault types are covered: device failure, fiber break, pigtail detachment, fiber bending, and connector loosen. Two key enhancements include sampling enhancement with fault-triggered 10ms-resolution power transient capture and a lightweight AI algorithm for millisecond-granularity diagnostics.\"},{\"question\":\"How does the passive WDM fronthaul approach distinguish fault layers?\",\"answer\":\"It uses AI multi-modal data fusion for root-cause discrimination with a two-stage process: fault classification and fault localization. Multi-modal inputs (e.g., temperature and optical power) improve discrimination accuracy versus an RNN baseline.\"}]","Intelligent Devices & Networking of Transport Networks: Evolution and Practice - AI-Driven Intelligent Sensing for Optical Fiber | PDF",53]