Channel Charting Based Beam SNR Prediction

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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.
Original languageEnglish
Title of host publication2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
Number of pages6
ISBN (Electronic)978-1-6654-1526-2
Publication statusPublished - 28 Jul 2021
MoE publication typeA4 Conference publication
EventEuropean Conference on Networks and Communications - Porto, Portugal
Duration: 8 Jun 202111 Jun 2021

Publication series

NameEuropean conference on networks and communications
ISSN (Print)2475-6490
ISSN (Electronic)2575-4912


ConferenceEuropean Conference on Networks and Communications
Abbreviated titleEuCNC


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