{"id":3754,"date":"2019-07-29T06:20:14","date_gmt":"2019-07-29T06:20:14","guid":{"rendered":"http:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3754"},"modified":"2020-09-04T07:01:56","modified_gmt":"2020-09-04T07:01:56","slug":"learning-based-remote-channel-inference-feasibility-analysis-and-case-study","status":"publish","type":"post","link":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/?p=3754","title":{"rendered":"Learning-Based Remote Channel Inference: Feasibility Analysis and Case Study"},"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 TRANSACTIONS ON WIRELESS COMMUNICATIONS\uff0c<\/em><\/strong>Vol: 18 No: 7 pp: 3554-3568<\/span><\/p>\n<p><span class=\"paper_subtitle\"><span class=\"paper_subtitle\">Published Date<\/span>: JUL 2019<\/span><\/p>\n<p><span class=\"paper_subtitle\">ABSTRACT<\/span><\/p>\n<p>Channel state information (CSI) plays a vital role in wireless communication systems. However, the CSI acquisition overhead is an enormous obstacle to realize the system\u00a0 performance improvements promised by massive connectivity and massive multiple-input-multiple-output (MIMO). To alleviate this overhead, this paper proposes a remote channel inference framework by probing the channels occupied by a source base station (BS) and inferring the channels of target BSs at geographically separated sites. The work generalizes existing literature which mainly focuses on utilizing the CSI linear correlations\u00a0 of adjacent antennas, by adopting a model-free deep learning framework to investigate non-linear dependence among remote CSI. The existence of such cross-BS CSI dependence is first shown by calculating the mutual information between remote channels, and the Cram\u00e9r-Rao lower bound of remote CSI inference performance based on a one-ring channel model. Inspired by this finding, modern deep learning approaches are leveraged to perform remote channel inference in heterogeneous networks for both single user and multi-user scenarios. The simulation results based on ray tracing data show evident performance advantages over conventional methods, under both homogeneous and heterogeneous frequency coverage. The proposed framework achieves beamformer inference accuracy within 4.6% of the genie-aided optimum at the cost of sweeping only two beams.<\/p>\n","protected":false},"excerpt":{"rendered":"<p><a href=\"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/wp-content\/uploads\/2020\/04\/Learning-Based-Remote-Channel-Inference-Feasibility-Analysis-and-Case-Study.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>\tSheng Chen, Zhiyuan Jiang, Sheng Zhou, Zhisheng Niu, Learning-Based Remote Channel Inference: Feasibility Analysis and Case Study, <span class=\"papersource\">IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS\uff0cVol: 18 No: 7 pp: 3554-3568, JUL 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\/3754"}],"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=3754"}],"version-history":[{"count":1,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3754\/revisions"}],"predecessor-version":[{"id":3811,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=\/wp\/v2\/posts\/3754\/revisions\/3811"}],"wp:attachment":[{"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3754"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3754"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/network.ee.tsinghua.edu.cn\/niulab\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3754"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}