{"id":2149,"date":"2010-12-19T21:02:37","date_gmt":"2010-12-19T21:02:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=2149"},"modified":"2010-12-19T21:02:37","modified_gmt":"2010-12-19T21:02:37","slug":"implementation-of-802-11n-on-128-core-processor","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2149","title":{"rendered":"Implementation of 802.11n on 128-CORE Processor"},"content":{"rendered":"<p>This article presents the results of a research in applying modern Graphics Processing Units in the field of telecommunications. The most recent Wireless Local Area Network protocol, 802.11n, was studied, as it introduces a significant increase of computational complexity. Taking into consideration the concept of Software Defined Radio, the implementation of PHY algorithms was devised on a modern programmable 128-core GPU. It was shown that a significant performance increase could be obtained through applying this novel approach. Our experiments had shown more than 8x performance boost. The article also discusses the approach of utilizing a GPU as a networking device. The author shows how a GPU could be effectively used as a Digital Signal Processing unit of a real communication device. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article presents the results of a research in applying modern Graphics Processing Units in the field of telecommunications. The most recent Wireless Local Area Network protocol, 802.11n, was studied, as it introduces a significant increase of computational complexity. Taking into consideration the concept of Software Defined Radio, the implementation of PHY algorithms was devised [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3,41],"tags":[1782,14,20,183,1789],"class_list":["post-2149","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","category-signal-processing","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-signal-processing"],"views":1939,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2149","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2149"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2149\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2149"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2149"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2149"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}