Beam SNR Prediction Using Channel Charting

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2023-10-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
16
1-16
Series
IEEE Transactions on Vehicular Technology
Abstract
We consider a machine learning approach for beam handover in mmWave 5G New Radio systems, in which User Equipments (UEs) perform autonomous beam selection, conditioned on a used Base Station (BS) beam. We develop a network-centric approach for predicting beam Signal-to-Noise Ratio (SNR) from Channel State Information (CSI) features measured at the BS, which consists of two phases; offline and online. In the offline training phase, we construct CSI features and dimensionality-reduced Channel Charts (CCs). We annotate the CCs with per-beam SNRs for different combinations of a BS beam and the corresponding best UE beam, and train models to predict SNR from CSI features for different BS/UE beam combinations. In the online phase, we predict SNRs of beam combinations not being used at the moment. We develop a low complexity out-of-sample algorithm for dimensionality reduction in the online phase. We consider K-nearest neighbors, Gaussian process regression, and neural network-based predictions. To evaluate the efficacy of the proposed framework, we perform simulations for a street segment with synthetically generated CSI. We investigate the complexity-accuracy trade-off for different dimensionality reduction techniques and different predictors. Our results reveal that nonlinear dimensionality reduction of CSI features with neural network prediction shows the best performance, and the performance of the best CSI-based prediction method is comparable to prediction based on using known physical location.
Description
Publisher Copyright: IEEE
Keywords
Antenna arrays, Antenna measurements, Array signal processing, beam SNR prediction, Beam-management, complexity analysis, CSI feature, dimensionality reduction techniques, Feature extraction, Handover, Millimeter wave communication, Neural Network, Signal to noise ratio, SNR prediction
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Citation
Kazemi, P, Al-Tous, H, Ponnada, T, Studer, C & Tirkkonen, O 2023, ' Beam SNR Prediction Using Channel Charting ', IEEE Transactions on Vehicular Technology, vol. 72, no. 10, pp. 13130-13145 . https://doi.org/10.1109/TVT.2023.3275280