Data-Driven Beams Selection for Beamspace Channel Estimation in Massive MIMO

Roman Bychkov, Alexander Osinsky, Andrey Ivanov, Dmitry Yarotsky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we present a new beam selection approach for the beamspace channel estimation (CE) in 64 antennas Massive Multiple-Input Multiple-Output (MIMO) receiver. Usually, the beamspace CE is implemented via digital transformation of antenna signal to a priori selected sub-space of discrete Fourier transform (DFT) directed towards propagation channel taps. This results in less complexity of CE and MIMO detector units. We propose a new data-based sub-space selection method, which outperforms the DFT-based beam selection thanks to employing prior knowledge of channel tap distribution in the spatial domain. Simulation results are presented for the non-line-of-sight models of the 5G QuaDRiGa 2.0 channel.

Original languageEnglish
Title of host publication2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189642
DOIs
Publication statusPublished - Apr 2021
Event93rd IEEE Vehicular Technology Conference, VTC 2021-Spring - Virtual, Online
Duration: 25 Apr 202128 Apr 2021

Publication series

NameIEEE Vehicular Technology Conference
Volume2021-April
ISSN (Print)1550-2252

Conference

Conference93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
CityVirtual, Online
Period25/04/2128/04/21

Keywords

  • Beamspace channel estimation
  • Massive MIMO

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