[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121238-en":3,"doc-seo-121238-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121238,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning opportunities for integrated polarization sensing and communication in optical fibers - opportunities for ISAC enhancement","Optical fibers, as the Internet’s backbone, are increasingly used for environmental sensing by exploiting their sensitivity to disturbances. This work studies integrated sensing and communication (ISAC) systems that monitor the state of polarization to detect environmental changes while also supporting data transmission. It investigates machine learning methods to improve polarization-based ISAC performance, including gradient-based techniques and adaptive equalization trade-offs. The study also reviews variational-autoencoder approaches for blind channel estimation, distributed polarization sensing via physics-based Jones matrix factorization, and dual-functional autoencoders for optimizing ISAC transmitters and waveforms.","Optical Fiber Technology 90 (2025) 104047  \n| Machine learning opportunities for integrated polarization sensing and communication in optical fibers\u003Cbr>Andrej Rode a ,∗,1 , Mohammad Farsi b, Vincent Lauingera, Magnus Karlsson c, Erik Agrellb, Laurent Schmalena, Christian Häger b\u003Cbr>a Communications Engineering Lab (CEL), Karlsruhe Institute of Technology, Karlsruhe, Germany b Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden c Department of Microtechnology and Nanoscience, Chalmers University of Technology, Gothenburg, Sweden |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>End-to-end autoencoders Machine learning Physics-based learning Polarization sensing Variational autoencoders |  | As the bedrock of the Internet, optical fibers are ubiquitously deployed and historically dedicated to ensuring robust data transmission. Leveraging their extensive installation, recent endeavors have focused on utilizing these telecommunication fibers also for environmental sensing, exploiting their inherent sensitivity to various environmental disturbances. In this paper, we consider integrated sensing and communication (ISAC) systems that combine data transmission and sensing functionalities, by monitoring the state of polarization to detect environmental changes. In particular, we investigate various machine learning techniques to enhance the performance and capabilities of such polarization-based ISAC systems. Gradient-based techniques such as adaptive zero-forcing equalization are examined for their potential to enhance sensing accuracy at the expense of communication performance, with strategies discussed for mitigating this trade-off. Additionally, the paper reviews novel machine-learning-based approaches for blind channel estimation using variational autoencoders, aimed at improving channel estimates compared to traditional adaptive equalization methods. We also discuss the problem of distributed polarization sensing and review a recent physics-based learning approach for Jones matrix factorization, potentially enabling spatial resolution of sensed events. Lastly, we discuss the potential of leveraging dual-functional autoencoders to optimize ISAC transmitters and the corresponding transmit waveforms. Our paper underscores the potential of telecom fibers for joint data transmission and environmental sensing, facilitated by advancements in digital signal processing and machine learning. |\n\n1. Introduction  \nWith the emergence of optical fiber communications as the backbone of the Internet, optical fibers are deployed virtually everywhere. While their primary use is to ensure reliable data transmission, many recent studies have investigated the possibility of repurposing such fibers into environmental sensors [1–3]. Indeed, optical fibers have been harnessed for dedicated sensing applications for several decades, playing an important role in environments that require sensitive, precise, and rapid monitoring [4–9].  \nTraditional fiber sensing works by launching a probing signal into the fiber and examining the subsequent backscattered light. This backscattered light carries the imprint of various external influences such as changes in temperature, pressure, or mechanical strain. By translating time-of-flight information into distances, one can further pinpoint  \nwhere a specific disturbance occurred along the fiber’s length. This feature, known as distributed optical fiber sensing (DOFS), has revolutionized various industries with applications ranging from monitoring pressure variations in pipelines to overseeing structural integrity of critical infrastructures [10–12].  \nWhereas dedicated fiber sensing applications utilize fibers not intended for simultaneous data transmission, telecom fibers can be buried deep beneath the ground, lie on the ocean floor surrounded by massive amounts of protective shielding, or simply hang in the air suspended from poles. E","cbCaiuHmcb2VEjvq","https://ap.wps.com/l/cbCaiuHmcb2VEjvq","pdf",1553347,1,12,"English","en",105,"# Introduction\n## Optical fiber sensing and distributed sensing (DOFS)\n## Motivation for integrated sensing and communication (ISAC)\n## State of polarization (SOP) in communication receivers","[{\"question\":\"What problem does the paper address in optical fiber sensing?\",\"answer\":\"It addresses how to use telecom optical fibers not only for reliable data transmission but also as environmental sensors by sensing changes through the state of polarization.\"},{\"question\":\"How does the paper approach integrated sensing and communication (ISAC)?\",\"answer\":\"It considers ISAC systems where sensing is built on standard digital signal processing blocks in coherent optical receivers, focusing on continuously estimating the signal’s SOP.\"},{\"question\":\"What machine learning contributions are discussed?\",\"answer\":\"It reviews learning-based methods such as gradient-based techniques for improving sensing accuracy, variational-autoencoder methods for blind channel estimation, physics-based learning for Jones matrix factorization in distributed sensing, and dual-functional autoencoders for ISAC transmitter optimization.\"}]","Machine learning opportunities for integrated polarization sensing and communication in optical fibers - opportunities for ISAC enhancement | PDF",1785734503,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-opportunities-for-integrated-polarization-sensing-and-communication-in-optical-fibers-opportunities-for-isac-enhancement","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-opportunities-for-integrated-polarization-sensing-and-communication-in-optical-fibers-opportunities-for-isac-enhancement/121238/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in optical fiber sensing?","Question",{"text":75,"@type":76},"It addresses how to use telecom optical fibers not only for reliable data transmission but also as environmental sensors by sensing changes through the state of polarization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper approach integrated sensing and communication (ISAC)?",{"text":80,"@type":76},"It considers ISAC systems where sensing is built on standard digital signal processing blocks in coherent optical receivers, focusing on continuously estimating the signal’s SOP.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning contributions are discussed?",{"text":84,"@type":76},"It reviews learning-based methods such as gradient-based techniques for improving sensing accuracy, variational-autoencoder methods for blind channel estimation, physics-based learning for Jones matrix factorization in distributed sensing, and dual-functional autoencoders for ISAC transmitter optimization.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]