Nonlinear-Feature Learned K-R Receiver with MSE Decomposition for Low-Resolution ADC 5G mm Wave systems

Nonlinear-Feature Learned K-R Receiver with MSE Decomposition for Low-Resolution ADC 5G mm Wave 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 Research Article Nonlinear-Feature Learned K-R Receiver with MSE Decomposition for Low-Resolution ADC 5G mm Wave systems RamaKrishna Pasupuleti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9278612/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We show that a simple nonlinear feature expansion combined with per-slot least-squares adaptation can explain and mitigate quantisation distortion in low-resolution ADC 5G mmWave receivers. The proposed NL-feat K-R receiver decomposes equalization into a physics-based MMSE K step and a data-driven R step trained per slot from 136 DMRS pilots using closed-form least squares—no backpropagation, no offline data. By appending quadratic terms [Re²,Im²,Re·Im] that capture the dominant second-order ADC distortion structure, the 9-feature model improves over the 4-feature baseline by + 0.070 bps/Hz at SNR = 20 dB. Proposition 1 provides an orthogonal decomposition of the residual error into a quantisation-floor component (γ_Q) and a channel-error projection component (γ_CE), offering interpretable insight into the gain mechanism. Simulations over 5,000 Monte Carlo trials (3GPP CDL-A, 4-bit ADC, 64-QAM, CR = 2/3) demonstrate statistically significant gains over all baselines: +0.197 bps/Hz over MMSE (p < 0.001), + 0.220 bps/Hz over OAMP-Net (p < 0.001)—which tracks near-MMSE as predicted by orthogonal AMP theory under non-Gaussian noise [ 14 ]—and + 1.387 bps/Hz over DetNet, whose gradient projection is not calibrated for continuous OFDM equalization with quantisation. SE gains are maintained at p < 0.001 for all UE speeds from 0 to 500 km/h at 28 GHz. Cell Communication and Signaling 5G NR mmWave low-resolution ADC MMSE nonlinear features learned receiver OAMP-Net DetNet MSE bound LDPC Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterialFinal.docx Supplementary Material Final generatefigures.py Genarate Figures Code Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9278612","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615207148,"identity":"4b65b331-be62-4e90-b313-00a4da576dbe","order_by":0,"name":"RamaKrishna Pasupuleti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYHACZoYEBjkgfbD9wwcgxcZOnBZjBgbGw22MM0BamInRwgDSwny8jZkHxscHDI73PjZ48McgcTvbwbbHNr+2yfMxMzB++JiDR8uZ48YJiW0GiTt7DrYb5/bdNmxjZmCWnLkNtxazG2nMBxIb/iRuuHGwQTq35zYjUAsbMy8hLQlAh224/7BB2rLntj1RWhIS2IBaDhxsk2b4cTuRoBb7M8eYDYB+MQZqaTbsbbid3MbM2IzXL5LtbcySP/4YyG44cPzhgx9/btvOb28++OEjHi2ogLENTDYQqx4E/pCieBSMglEwCkYKAAAdgVk9CekE7wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-8418-1430","institution":"Kakatiya University","correspondingAuthor":true,"prefix":"","firstName":"RamaKrishna","middleName":"","lastName":"Pasupuleti","suffix":""}],"badges":[],"createdAt":"2026-03-31 10:47:47","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9278612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9278612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105906793,"identity":"0fb58bd4-50ca-4454-a52b-c464ab255be6","added_by":"auto","created_at":"2026-04-01 10:24:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":459825,"visible":true,"origin":"","legend":"","description":"","filename":"KRSPLMain.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9278612/v1_covered_6670fd7c-6533-4999-8a5f-7f1a91e58bd4.pdf"},{"id":105905086,"identity":"75fc7cda-cae8-458d-bbcc-83dedcf9fad7","added_by":"auto","created_at":"2026-04-01 10:11:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38930,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material Final\u003c/p\u003e","description":"","filename":"SupplementaryMaterialFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-9278612/v1/6522288703e2434437217490.docx"},{"id":105866642,"identity":"7ab6a7ac-f875-4edd-a753-6d0fc305672d","added_by":"auto","created_at":"2026-04-01 03:13:25","extension":"py","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25628,"visible":true,"origin":"","legend":"\u003cp\u003eGenarate Figures Code\u003c/p\u003e","description":"","filename":"generatefigures.py","url":"https://assets-eu.researchsquare.com/files/rs-9278612/v1/b6c80de992257f5145a0ce7a.py"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eNonlinear-Feature Learned K-R Receiver with MSE Decomposition for Low-Resolution ADC 5G mm Wave systems\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"5G NR, mmWave, low-resolution ADC, MMSE, nonlinear features, learned receiver, OAMP-Net, DetNet, MSE bound, LDPC","lastPublishedDoi":"10.21203/rs.3.rs-9278612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9278612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe show that a simple nonlinear feature expansion combined with per-slot least-squares adaptation can explain and mitigate quantisation distortion in low-resolution ADC 5G mmWave receivers. The proposed NL-feat K-R receiver decomposes equalization into a physics-based MMSE K step and a data-driven R step trained per slot from 136 DMRS pilots using closed-form least squares\u0026mdash;no backpropagation, no offline data. By appending quadratic terms [Re\u0026sup2;,Im\u0026sup2;,Re\u0026middot;Im] that capture the dominant second-order ADC distortion structure, the 9-feature model improves over the 4-feature baseline by +\u0026thinsp;0.070 bps/Hz at SNR\u0026thinsp;=\u0026thinsp;20 dB. Proposition 1 provides an orthogonal decomposition of the residual error into a quantisation-floor component (γ_Q) and a channel-error projection component (γ_CE), offering interpretable insight into the gain mechanism. Simulations over 5,000 Monte Carlo trials (3GPP CDL-A, 4-bit ADC, 64-QAM, CR\u0026thinsp;=\u0026thinsp;2/3) demonstrate statistically significant gains over all baselines: +0.197 bps/Hz over MMSE (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), +\u0026thinsp;0.220 bps/Hz over OAMP-Net (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u0026mdash;which tracks near-MMSE as predicted by orthogonal AMP theory under non-Gaussian noise [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u0026mdash;and +\u0026thinsp;1.387 bps/Hz over DetNet, whose gradient projection is not calibrated for continuous OFDM equalization with quantisation. SE gains are maintained at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all UE speeds from 0 to 500 km/h at 28 GHz.\u003c/p\u003e","manuscriptTitle":"Nonlinear-Feature Learned K-R Receiver with MSE Decomposition for Low-Resolution ADC 5G mm Wave systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 03:13:21","doi":"10.21203/rs.3.rs-9278612/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dafb1023-9678-48dc-92fe-bcfd0983ee2d","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65461386,"name":"Cell Communication and Signaling"}],"tags":[],"updatedAt":"2026-04-01T03:13:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 03:13:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9278612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9278612","identity":"rs-9278612","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}