Channel Charting Based Beam SNR Prediction

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorKazemi, Parhamen_US
dc.contributor.authorPonnada, Tusharaen_US
dc.contributor.authorAl-Tous, Hananen_US
dc.contributor.authorLiang, Ying-Changen_US
dc.contributor.authorTirkkonen, Olaven_US
dc.contributor.departmentDepartment of Communications and Networkingen
dc.contributor.groupauthorCommunications Theoryen
dc.contributor.organizationUniversity of Electronic Science and Technology of Chinaen_US
dc.date.accessioned2021-09-02T08:47:36Z
dc.date.available2021-09-02T08:47:36Z
dc.date.issued2021-07-28en_US
dc.description| openaire: EC/H2020/813999/EU//WINDMILL
dc.description.abstractWe 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.description.versionPeer revieweden
dc.format.extent6
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationKazemi, 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/6GSummit51104.2021.9482548en
dc.identifier.doi10.1109/EuCNC/6GSummit51104.2021.9482548en_US
dc.identifier.isbn978-1-6654-1526-2
dc.identifier.issn2475-6490
dc.identifier.issn2575-4912
dc.identifier.otherPURE UUID: e59ac2e4-a9b7-4b45-8f3b-1ea2b4cd8f6aen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/e59ac2e4-a9b7-4b45-8f3b-1ea2b4cd8f6aen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85112647684&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/64983956/ELEC_Kazemi_etal_Channel_Charting_Based_Beam_SNR_Prediction.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/109623
dc.identifier.urnURN:NBN:fi:aalto-202109028855
dc.language.isoenen
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/813999/EU//WINDMILLen_US
dc.relation.ispartofEuropean Conference on Networks and Communicationsen
dc.relation.ispartofseries2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)en
dc.relation.ispartofseriesEuropean conference on networks and communicationsen
dc.rightsopenAccessen
dc.titleChannel Charting Based Beam SNR Predictionen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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