Channel Charting Based Beam SNR Prediction

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Conference article in proceedings
Date
2021-07-28
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Language
en
Pages
6
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2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), European conference on networks and communications
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.
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| openaire: EC/H2020/813999/EU//WINDMILL
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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