{"id":3699,"date":"2018-11-14T07:55:07","date_gmt":"2018-11-14T07:55:07","guid":{"rendered":"http:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3699"},"modified":"2020-09-04T07:19:57","modified_gmt":"2020-09-04T07:19:57","slug":"time-sequence-channel-inference-for-beam-alignment-in-vehicular-networks","status":"publish","type":"post","link":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3699","title":{"rendered":"Time-sequence channel inference for beam alignment in vehicular networks"},"content":{"rendered":"<p><span class=\"paper_subtitle\">LANGUAGE English <\/span><\/p>\n<p><span class=\"paper_subtitle\"><span class=\"paper_subtitle\">SOURCE<\/span>\u00a0<strong><em>IEEE GlobalSIP\u201918<\/em><\/strong>, Anaheim, Nov.,26-29\uff0c2018<\/span><\/p>\n<p><span class=\"paper_subtitle\"><span class=\"paper_subtitle\">Published Date<\/span>:2018-11 <\/span><\/p>\n<p><span class=\"paper_subtitle\">ABSTRACT<\/span><\/p>\n<p>In this paper, we propose a learning-based low-overhead<br \/>\nbeam alignment method for vehicle-to-infrastructure communication<br \/>\nin vehicular networks. The main idea is to remotely<br \/>\ninfer the optimal beam directions at a target base station in<br \/>\nfuture time slots, based on the CSI of a source base station<br \/>\nin previous time slots. The proposed scheme can reduce<br \/>\nchannel acquisition and beam training overhead by replacing<br \/>\npilot-aided beam training with online inference from a<br \/>\nsequence-to-sequence neural network. Simulation results<br \/>\nbased on ray-tracing channel data show that our proposed<br \/>\nscheme achieves a 8:86% improvement over location-based<br \/>\nbeamforming schemes with a positioning error of 1m, and is<br \/>\nwithin a 4:93% performance loss compared with the genieaided<br \/>\noptimal beamformer.<\/p>\n","protected":false},"excerpt":{"rendered":"<p><a href=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2018\/10\/\u9648\u665fGlobalSIP.pdf\" target=\"_blank\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-117\" title=\"pdf\" src=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2010\/08\/pdf.gif\"alt=\"\" width=\"95\" height=\"50\" \/><\/a>Sheng Chen, Zhiyuan Jiang, Sheng Zhou, and Zhisheng Niu, Time-sequence channel inference for beam alignment in vehicular networks, <span class=\"papersource\">IEEE GlobalSIP\u201918, Anaheim, Nov.,26-29\uff0c2018<\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[7],"tags":[99],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3699"}],"collection":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3699"}],"version-history":[{"count":0,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3699\/revisions"}],"wp:attachment":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3699"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3699"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3699"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}