Channel Charting Based Beam SNR Prediction

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

3 Sitaatiot (Scopus)
94 Lataukset (Pure)


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.
Otsikko2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
ISBN (elektroninen)978-1-6654-1526-2
DOI - pysyväislinkit
TilaJulkaistu - 28 heinäk. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaEuropean Conference on Networks and Communications - Porto, Portugali
Kesto: 8 kesäk. 202111 kesäk. 2021


NimiEuropean conference on networks and communications
ISSN (painettu)2475-6490
ISSN (elektroninen)2575-4912


ConferenceEuropean Conference on Networks and Communications


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