{"id":8791,"date":"2013-01-16T00:33:48","date_gmt":"2013-01-15T22:33:48","guid":{"rendered":"http:\/\/hgpu.org\/?p=8791"},"modified":"2013-01-16T00:33:48","modified_gmt":"2013-01-15T22:33:48","slug":"on-the-use-of-graphic-processing-units-for-the-efficient-implementation-of-mimo-detectors","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8791","title":{"rendered":"On the Use of Graphic Processing Units for the Efficient Implementation of MIMO Detectors"},"content":{"rendered":"<p>The use of Graphic Processing Units (GPU) for the efficient implementation of signal processing algorithms for MIMO communication systems is receiving incremental attention recently. This is mainly due to their high capability of parallel processing together with their reasonable cost. In this work, the interest of GPU for the rapid prototyping of MIMO receivers is investigated. The GPU implementation of a fixed-complexity tree-search-based MIMO detector is presented and it shows to considerably decrease the execution time required to perform data detection with respect to a sequential implementation of this method in a high-performance CPU. In addition, the throughput of the implementation outperforms the one of other recent implementations while ensuring nearly optimal detection performance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The use of Graphic Processing Units (GPU) for the efficient implementation of signal processing algorithms for MIMO communication systems is receiving incremental attention recently. This is mainly due to their high capability of parallel processing together with their reasonable cost. In this work, the interest of GPU for the rapid prototyping of MIMO receivers is [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":[36,89,3,41],"tags":[1787,14,521,20,1789,1006],"class_list":["post-8791","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-paper","category-signal-processing","tag-algorithms","tag-cuda","tag-mimo","tag-nvidia","tag-signal-processing","tag-tesla-c2070"],"views":2035,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8791","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=8791"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8791\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8791"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8791"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8791"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}