{"id":18969,"date":"2020-12-03T13:51:30","date_gmt":"2020-12-03T12:51:30","guid":{"rendered":"https:\/\/networks.imdea.org\/?p=18969"},"modified":"2020-12-03T13:51:30","modified_gmt":"2020-12-03T12:51:30","slug":"best-paper-award-at-mswim-2020-for-a-research-made-at-imdea-networks","status":"publish","type":"post","link":"https:\/\/networks.imdea.org\/es\/best-paper-award-en-mswim-2020-a-una-investigacion-realizada-en-imdea-networks\/","title":{"rendered":"Best Paper Award en MSWIM 2020 a una investigaci\u00f3n realizada en IMDEA Networks"},"content":{"rendered":"<p>El art\u00edculo \u00ab<a href=\"http:\/\/eprints.networks.imdea.org\/2224\/1\/ACM_MSWiM_2020___TRK__Copy_%20%281%29.pdf\">A Mixture Density Channel Model for Deep Learning-Based Wireless Physical Layer Design<\/a>\u00bb, de Dolores Garc\u00eda Mart\u00ed, <a href=\"https:\/\/networks.imdea.org\/es\/equipo\/equipo-imdea-networks\/personas\/jesus-omar-lacruz\/\" target=\"_blank\" rel=\"noopener noreferrer\">Jes\u00fas Omar Lacruz<\/a>, <a href=\"https:\/\/networks.imdea.org\/es\/equipo\/equipo-imdea-networks\/personas\/joan-palacios\/\" target=\"_blank\" rel=\"noopener noreferrer\">Joan Palacios Beltr\u00e1n<\/a> y Joerg Widmer, ha sido galardonado con el premio al mejor art\u00edculo en la 23\u00aa Conferencia Internacional de Modelizaci\u00f3n, An\u00e1lisis y Simulaci\u00f3n de sistemas inal\u00e1mbricos y m\u00f3viles (MSWiM). Esta es una conferencia de alta calidad en el campo de las comunicaciones inal\u00e1mbricas.<\/p>\n<p>El aprendizaje autom\u00e1tico es una herramienta muy prometedora para dise\u00f1ar la capa f\u00edsica de los sistemas de comunicaci\u00f3n inal\u00e1mbrica, pero generalmente requiere que se conozca un modelo de canal. A medida que aumentan las velocidades de datos y los transceptores inal\u00e1mbricos se vuelven m\u00e1s complejos, el canal inal\u00e1mbrico, las imperfecciones del hardware y sus interacciones se vuelven m\u00e1s dif\u00edciles de modelar y compensar expl\u00edcitamente.<\/p>\n<p>Los nuevos esquemas de aprendizaje autom\u00e1tico para la capa f\u00edsica no requieren un modelo expl\u00edcito, pero aprenden impl\u00edcitamente el enlace de un extremo a otro, incluidas las caracter\u00edsticas del canal y las no linealidades del sistema directamente a partir de los datos de entrenamiento. \u201cUna posible aplicaci\u00f3n de esta investigaci\u00f3n ser\u00eda <strong>corregir el efecto de las imperfecciones del hardware<\/strong>. A medida que avanzamos hacia frecuencias m\u00e1s altas, estas imperfecciones de hardware se har\u00e1n m\u00e1s grandes y complejas. Este m\u00e9todo permite corregirlos sin tener que modelarlos matem\u00e1ticamente\u201d, dice <a href=\"https:\/\/networks.imdea.org\/es\/equipo\/equipo-imdea-networks\/personas\/dolores-garcia-marti\/\" target=\"_blank\" rel=\"noopener noreferrer\">Dolores Garc\u00eda Mart\u00ed<\/a>, primera autora de este art\u00edculo. Trabaja como estudiante de doctorado en IMDEA Networks dentro del grupo de Redes Inal\u00e1mbricas, encabezado por el profesor Joerg Widmer.<\/p>\n<p>Seg\u00fan su supervisor <a href=\"https:\/\/networks.imdea.org\/es\/equipo\/equipo-imdea-networks\/personas\/joerg-widmer\/\" target=\"_blank\" rel=\"noopener noreferrer\">Joerg Widmer<\/a>, director de investigaci\u00f3n de IMDEA Networks y coautor de esta investigaci\u00f3n, \u201cestas t\u00e9cnicas son extremadamente prometedoras para <strong>facilitar la integraci\u00f3n de futuras tecnolog\u00edas inal\u00e1mbricas<\/strong> de alta velocidad en dispositivos de IoT y de consumo econ\u00f3micos\u201d.<\/p>\n<p>Al ser preguntada por cu\u00e1l es el mayor desaf\u00edo al que se ha enfrentado para realizar esta investigaci\u00f3n, Garc\u00eda Mart\u00ed no tiene ninguna duda: \u201cNo conoc\u00eda los m\u00e9todos de aprendizaje profundo utilizados y tuve la oportunidad de desarrollarlos durante mi pasant\u00eda en Cambridge. Nuestro equipo se centra en demostrar una aplicaci\u00f3n real y <strong>probamos nuestros resultados en nuestro banco de pruebas de 60 GHz<\/strong>. \u00a1Fue un esfuerzo de equipo incre\u00edble!\u201d.<\/p>\n<h3><strong>Lo m\u00e1s destacado<\/strong><\/h3>\n<p>Los investigadores presentan una arquitectura de red neuronal novedosa que proporciona un modelo de canal estoc\u00e1stico expl\u00edcito, al aprender los par\u00e1metros de una distribuci\u00f3n de mezcla gaussiana a partir de muestras de canales reales. De esa manera, utilizan este modelo de canal junto con un codificador autom\u00e1tico para el dise\u00f1o de la capa f\u00edsica para aprender un esquema de modulaci\u00f3n adecuado.<\/p>\n<p>Dado que el sistema aprende un modelo expl\u00edcito para el canal, <strong>se puede utilizar el aprendizaje de transferencia para adaptarse m\u00e1s r\u00e1pidamente a los cambios en el entorno<\/strong>. El equipo ha aplicado el modelo a las comunicaciones de ondas milim\u00e9tricas con sus desaf\u00edos de arreglos en fase con una gran cantidad de antenas, frecuencias portadoras altas, ancho de banda amplio y caracter\u00edsticas de canal complejas. Sin embargo, han validado experimentalmente el sistema utilizando un banco de pruebas basado en FPGA de 60 GHz y han demostrado que es capaz de reproducir las caracter\u00edsticas del canal con buena precisi\u00f3n.<\/p>\n<p>\u201cEl nuevo trabajo que se est\u00e1 realizando en comunicaciones de extremo a extremo permite repensar los bloques de comunicaci\u00f3n y ser\u00e1 parte de la soluci\u00f3n para los escenarios m\u00e1s complejos que veremos en la pr\u00f3xima generaci\u00f3n de redes. Nuestro trabajo <strong>aporta m\u00e1s practicidad a las investigaciones previas<\/strong> ya que en lugar de muestrear el canal, obtenemos un modelo que permite extender el modelo cuando cambia el canal\u201d, concluye Garc\u00eda Mart\u00ed.<\/p>","protected":false},"excerpt":{"rendered":"El art\u00edculo \u00abA Mixture Density Channel Model for Deep Learning-Based Wireless Physical Layer Design\u00bb, de Dolores Garc\u00eda Mart\u00ed, Jes\u00fas Omar Lacruz, Joan Palacios Beltr\u00e1n y Joerg Widmer, ha sido galardonado con el premio al mejor art\u00edculo en la 23\u00aa Conferencia Internacional de Modelizaci\u00f3n, An\u00e1lisis y Simulaci\u00f3n de sistemas inal\u00e1mbricos y m\u00f3viles (MSWiM). Esta es una&#8230;","protected":false},"author":145,"featured_media":18971,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[464,473,465,494,489,476],"class_list":["post-18969","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categoria","tag-imdea-networks","tag-innovation","tag-internet","tag-machine-learning","tag-millimeter-wave-communication","tag-wireless-networking"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Best Paper Award en MSWIM 2020 a una investigaci\u00f3n realizada en IMDEA Networks - IMDEA Networks<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/networks.imdea.org\/es\/best-paper-award-en-mswim-2020-a-una-investigacion-realizada-en-imdea-networks\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Best Paper Award en MSWIM 2020 a una investigaci\u00f3n realizada en IMDEA Networks - IMDEA Networks\" \/>\n<meta property=\"og:description\" content=\"El art\u00edculo \u00abA Mixture Density Channel Model for Deep Learning-Based Wireless Physical Layer Design\u00bb, de Dolores Garc\u00eda Mart\u00ed, Jes\u00fas Omar Lacruz, Joan Palacios Beltr\u00e1n y Joerg Widmer, ha sido galardonado con el premio al mejor art\u00edculo en la 23\u00aa Conferencia Internacional de Modelizaci\u00f3n, An\u00e1lisis y Simulaci\u00f3n de sistemas inal\u00e1mbricos y m\u00f3viles (MSWiM). 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