{"id":3774,"date":"2019-03-29T07:59:22","date_gmt":"2019-03-29T07:59:22","guid":{"rendered":"http:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3774"},"modified":"2020-09-04T07:05:32","modified_gmt":"2020-09-04T07:05:32","slug":"exploiting-wireless-channel-state-information-structures-beyond-linear-correlations-a-deep-learning-approach","status":"publish","type":"post","link":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3774","title":{"rendered":"Exploiting wireless channel state information structures beyond linear correlations: A deep learning approach"},"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 COMMUNICATIONS MAGAZINE<\/em><\/strong>, Vol: 57 No: 3 pp: 28-34, MAR 2019<\/span><\/p>\n<p><span class=\"paper_subtitle\"><span class=\"paper_subtitle\">Published Date<\/span>: MAR 2019<\/span><\/p>\n<p><span class=\"paper_subtitle\">ABSTRACT<\/span><\/p>\n<p>Knowledge of information about the propagation channel in which a wireless system operates enables better, more efficient approaches\u00a0 for signal transmissions. Therefore, channel state information (CSI) plays a pivotal role in the system performance. The importance of CSI is in\u00a0 fact growing in the upcoming 5G and beyond systems, for example, for the implementation of massive multiple-input multiple-output (MIMO).\u00a0 However, the acquisition of timely and accurate CSI has long been\u00a0 onsidered a major issue, and becomes increasingly challenging due to\u00a0 the need for obtaining CSI of many antenna elements in massive MIMO systems. To cope with this challenge, existing works mainly focus on\u00a0 exploiting linear structures of CSI, such as CSI correlations in the spatial domain, to achieve dimensionality reduction. In this article, we first\u00a0 systematically review the state of the art on CSI structure exploitation. We then extend to seek deeper structures that enable remote CSI inference\u00a0 wherein a data-driven deep neural network (DNN) approach is necessary due to model inadequacy. We develop specific DNN designs suitable\u00a0 for CSI data. Case studies are provided to demonstrate great potential in this direction for future performance enhancement.<\/p>\n","protected":false},"excerpt":{"rendered":"<p><a href=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2019\/12\/Exploiting-wireless-channel-state-information-structures-beyond-linear-correlations-A-deep-learning-approach.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>\tZhiyuan Jiang, Sheng Chen, A. F. Molisch, R. Vannithamby, Sheng Zhou, and Zhisheng Niu, Exploiting wireless channel state information structures beyond linear correlations: A deep learning approach, <span class=\"papersource\">IEEE COMMUNICATIONS MAGAZINE, Vol: 57 No: 3 pp: 28-34, MAR 2019 <\/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":[100],"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\/3774"}],"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=3774"}],"version-history":[{"count":1,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3774\/revisions"}],"predecessor-version":[{"id":3805,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3774\/revisions\/3805"}],"wp:attachment":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3774"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3774"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3774"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}