Channel Charting Based Beam SNR Prediction

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.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.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 .
dc.identifier.otherPURE UUID: e59ac2e4-a9b7-4b45-8f3b-1ea2b4cd8f6aen_US
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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.titleChannel Charting Based Beam SNR Predictionen
dc.typeConference article in proceedingsfi