Indoor Positioning with Multi-domain CSI-based Deep Attention Networks for MIMO Wireless Systems

Indoor Positioning with Multi-domain CSI-based Deep Attention Networks for MIMO Wireless Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Indoor Positioning with Multi-domain CSI-based Deep Attention Networks for MIMO Wireless Systems Praneeth Susarla, Anirban Mukherjee, S. S. Krishna Chaitanya Bulusu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6817871/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Accurate indoor positioning is vital for applications such as augmented reality and autonomous robotics. Channel state information (CSI)-based methods, particularly when combined with beamforming, massive multiple input multiple output (mMIMO) techniques, and artificial intelligence (AI) algorithms, offer enhanced indoor user equipment (UE) positioning accuracy and robustness in complex indoor environments. In this paper, we present an AI-driven CSI-based indoor positioning method for mMIMO systems, where time, frequency, and Doppler features are extracted from the CSI data and combined to form both uni-domain and multi-domain feature sets. We introduce a deep attention network (DAN), an AI algorithm that leverages attention mechanisms to effectively integrate and process multi-domain CSI data for enhanced UE positioning performance. We evaluate DAN using a publicly available mMIMO dataset and compare its performance against the baseline and multi-domain convolutional neural network (CNN) models. Our results show that multi-domain DAN outperforms CNN approaches in positioning accuracy, though at the cost of increased inference complexity—highlighting a trade-off between performance and computational overhead. These findings demonstrate the potential of attention mechanisms and multi-domain CSI features for accurate indoor UE positioning systems. Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing 5G and beyond 6G AI/ML Channel state information (CSI) Deep attention network Indoor positioning and Multi-input multi-output (MIMO) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviewers invited by journal 06 Jul, 2025 Editor assigned by journal 10 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 04 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6817871","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":482560900,"identity":"037d69d0-782e-4962-8040-13f24ff84dde","order_by":0,"name":"Praneeth Susarla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBCDBChtwcDPwMB4gIFBjoeADgOYFgkGyQYGBqAWYxK0GByAaMGp1lz68LMHHyr+5DHwnzH+8HOPhLzx8R6DwzwMBjK4tFj2pZkbzjhjUMwgkWMm2fNMwnDbmTNgLTgdZnCGwUyat80gsUGCx4yB54AE47YbOSAtf/BoYf8m/fcfUAvQYR//HJCw3zwjh5AtPGbSjA1ALQw5BtJAWxI3SBDQYtnDUybZc8w4sU0irUxa5oBE8owzxwoOzjHArcWch32bxI8aucR+/sObP745YGPb39688cGbCgN7nA6DMdhwiOPRMgpGwSgYBaMAJwAAigBQgb4RjGYAAAAASUVORK5CYII=","orcid":"","institution":"University of Oulu","correspondingAuthor":true,"prefix":"","firstName":"Praneeth","middleName":"","lastName":"Susarla","suffix":""},{"id":482560901,"identity":"a8890811-81e6-4141-9cb5-4d0533a3cbbb","order_by":1,"name":"Anirban Mukherjee","email":"","orcid":"","institution":"IIIT Bangalore","correspondingAuthor":false,"prefix":"","firstName":"Anirban","middleName":"","lastName":"Mukherjee","suffix":""},{"id":482560902,"identity":"cd224224-5328-41a2-9a9e-659bd746ce53","order_by":2,"name":"S. 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Channel state information (CSI)-based methods, particularly when combined with beamforming, massive multiple input multiple output (mMIMO) techniques, and artificial intelligence (AI) algorithms, offer enhanced indoor user equipment (UE) positioning accuracy and robustness in complex indoor environments. In this paper, we present an AI-driven CSI-based indoor positioning method for mMIMO systems, where time, frequency, and Doppler features are extracted from the CSI data and combined to form both uni-domain and multi-domain feature sets. We introduce a deep attention network (DAN), an AI algorithm that leverages attention mechanisms to effectively integrate and process multi-domain CSI data for enhanced UE positioning performance. We evaluate DAN using a publicly available mMIMO dataset and compare its performance against the baseline and multi-domain convolutional neural network (CNN) models. Our results show that multi-domain DAN outperforms CNN approaches in positioning accuracy, though at the cost of increased inference complexity—highlighting a trade-off between performance and computational overhead. These findings demonstrate the potential of attention mechanisms and multi-domain CSI features for accurate indoor UE positioning systems.","manuscriptTitle":"Indoor Positioning with Multi-domain CSI-based Deep Attention Networks for MIMO Wireless Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 07:57:31","doi":"10.21203/rs.3.rs-6817871/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-17T10:57:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T06:46:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T07:49:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334981506770552648484316181818223709383","date":"2025-09-01T09:03:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336096410908804724531246171253363607489","date":"2025-09-01T08:21:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17336668053928833764585850469406642350","date":"2025-09-01T08:17:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T14:29:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255447030197099821836609472888364305144","date":"2025-07-06T09:37:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-06T04:08:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T15:22:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-09T09:39:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Wireless Technology","date":"2025-06-04T07:52:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-wireless-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Wireless Technology](https://www.nature.com/npjwireltech)","snPcode":"44459","submissionUrl":"https://submission.springernature.com/new-submission/44459/3?","title":"npj Wireless Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"038addda-144a-4831-9d76-764d74e7d1ac","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51237649,"name":"Physical sciences/Mathematics and computing/Information technology"},{"id":51237650,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-15T12:39:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-11 07:57:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6817871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6817871","identity":"rs-6817871","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}