{"id":13803,"date":"2015-04-01T23:32:33","date_gmt":"2015-04-01T20:32:33","guid":{"rendered":"http:\/\/hgpu.org\/?p=13803"},"modified":"2015-04-01T23:32:33","modified_gmt":"2015-04-01T20:32:33","slug":"distributed-wideband-software-defined-radio-receiver-for-heterogeneous-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13803","title":{"rendered":"Distributed wideband software-defined radio receiver for heterogeneous systems"},"content":{"rendered":"<p>Recent years have seen an increasing need for computationally efficient implementation of software-defined radio (SDR) systems. Given the limitations of a typical SDR application running on a single machine, we present a distributed SDR system using high-performance techniques. To split a digital signal into multiple channels, we use an efficient digital signal processing technique: a channelizer. Distributed machines then process the channelized streams. To achieve this, we implement a load-balancer module and add new internet protocol communication capabilities to an existing SDR framework. Since the channelizer cannot be efficiently distributed, a single host machine has to process the entire wide-band signal. Furthermore, we optimize an existing implementation of a CPU channelizer and implement a new original GPU channelizer to push the system limitations further. The described techniques are applied to and tested with an existing implementation of a passive GSM receiver to add a full-band capability.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recent years have seen an increasing need for computationally efficient implementation of software-defined radio (SDR) systems. Given the limitations of a typical SDR application running on a single machine, we present a distributed SDR system using high-performance techniques. To split a digital signal into multiple channels, we use an efficient digital signal processing technique: a [&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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[90,3,41],"tags":[1602,7,452,1793,1789,390],"class_list":["post-13803","post","type-post","status-publish","format-standard","hentry","category-opencl","category-paper","category-signal-processing","tag-amd-radeon-r9-290","tag-ati","tag-heterogeneous-systems","tag-opencl","tag-signal-processing","tag-thesis"],"views":8118,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13803","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=13803"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13803\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13803"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13803"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13803"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}