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Channel Charting Based Beam SNR Prediction

Authors :
Olav Tirkkonen
Hanan Al-Tous
Parham Kazemi
Ying-Chang Liang
Tushara Ponnada
Communications Theory
University of Electronic Science and Technology of China
Department of Communications and Networking
Aalto-yliopisto
Aalto University
Source :
EuCNC/6G Summit
Publication Year :
2021

Abstract

openaire: EC/H2020/813999/EU//WINDMILL 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.

Details

Language :
English
Database :
OpenAIRE
Journal :
EuCNC/6G Summit
Accession number :
edsair.doi.dedup.....ecd91ae5d1c162138c39688fe33a1f2c