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Channel Charting Based Beam SNR Prediction

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Kazemi, Parham
dc.contributor.author Ponnada, Tushara
dc.contributor.author Al-Tous, Hanan
dc.contributor.author Liang, Ying-Chang
dc.contributor.author Tirkkonen, Olav
dc.date.accessioned 2021-09-02T08:47:36Z
dc.date.available 2021-09-02T08:47:36Z
dc.date.issued 2021-07-28
dc.identifier.citation Kazemi , P , Ponnada , T , Al-Tous , H , Liang , Y-C & Tirkkonen , O 2021 , Channel Charting Based Beam SNR Prediction . in 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) . , 9482548 , European conference on networks and communications , IEEE , pp. 72-77 , European Conference on Networks and Communications , Porto , Portugal , 08/06/2021 . https://doi.org/10.1109/EuCNC/6GSummit51104.2021.9482548 en
dc.identifier.isbn 978-1-6654-1526-2
dc.identifier.issn 2475-6490
dc.identifier.issn 2575-4912
dc.identifier.other PURE UUID: e59ac2e4-a9b7-4b45-8f3b-1ea2b4cd8f6a
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/e59ac2e4-a9b7-4b45-8f3b-1ea2b4cd8f6a
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85112647684&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/64983956/ELEC_Kazemi_etal_Channel_Charting_Based_Beam_SNR_Prediction.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/109623
dc.description | openaire: EC/H2020/813999/EU//WINDMILL
dc.description.abstract We consider machine learning for intra cell beam handovers in mmWave 5GNR systems by leveraging Channel Charting (CC). We develop a base station centric approach for predicting the Signal-to-Noise-Ratio (SNR) of beams. Beam SNRs are predicted based on measured signal at the BS without the need to exchange information with UEs. In an offline training phase, we construct a beam-specific dimensionality reduction of Channel State Information (CSI) to a low-dimensional CC, annotate the CC with beam-wise SNRs and then train SNR predictors for different target beams. In the online phase, we predict target beam SNRs. K-nearest neighbors, Gaussian Process Regression and Neural Network based prediction are considered. Based on SNR difference between the serving and target beams a handover can be decided. To evaluate the efficiency of the proposed framework, we perform simulations for a street segment with synthetically generated CSI. SNR prediction accuracy of average root mean square error less than 0.3 dB is achieved. en
dc.format.extent 6
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/813999/EU//WINDMILL
dc.relation.ispartof European Conference on Networks and Communications en
dc.relation.ispartofseries 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) en
dc.relation.ispartofseries European conference on networks and communications en
dc.rights openAccess en
dc.title Channel Charting Based Beam SNR Prediction en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Communications Theory
dc.contributor.department University of Electronic Science and Technology of China
dc.contributor.department Department of Communications and Networking
dc.identifier.urn URN:NBN:fi:aalto-202109028855
dc.identifier.doi 10.1109/EuCNC/6GSummit51104.2021.9482548
dc.type.version acceptedVersion

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