Time-Varying Channel Estimation Scheme for Uplink MU-MIMO in 6G Systems

Jianhao Wang, Wensheng Zhang, Yunfei Chen, Zhi Liu, Jian Sun, Cheng Xiang Wang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Channel estimation is a challenging issue for millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) in the future sixth generation (6G) wireless systems, where the conventional estimation schemes may fail to track the fast varying channels, especially in high-speed mobile scenarios. In this paper, a novel tensor-based uplink channel estimation scheme is proposed for multi-user MIMO (MU-MIMO) systems over time-varying channels. In the proposed scheme, a low-overhead pilot transmission scheme is designed to track the varying channel. The received uplink signal at the base station (BS) is formulated as a third-order tensor which admits a CANDECOMP/PARAFAC (CP) model. The CP decomposition issue is then solved using blind matrix decomposition, in which the special structures of signals in the time dimension and the matrix subspace are utilized. By exploiting low-rank structure of the signal tensor, the channel parameters (angles of arrival/departure, path gains, and Doppler shifts) are estimated from the factor matrices. Moreover, the proposed scheme is theoretically analyzed and it is guaranteed with low pilot overhead. Simulation results verify that the proposed scheme can outperform the compressed sensing (CS) based scheme and the iteration-based scheme in terms of accuracy and stability. The uniqueness of the proposed scheme is also verified in our simulation.
Original languageEnglish (US)
Pages (from-to)11820-11831
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Volume71
Issue number11
DOIs
StatePublished - Jul 21 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-12-23
Acknowledged KAUST grant number(s): ORA-2021-CRG10-4696
Acknowledgements: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 62071276, 61960206006, and 61771293, in part by the Key Technologies RandD Program of Jiangsu (Prospective and Key Technologies for Industry) under Grants BE2022067 and BE2022067-1, in part by the King Abdullah University of Science and Technology Research Funding under Grant ORA-2021-CRG10-4696, in part by the Natural Science Foundation of Shandong Province under Grants ZR2020MF002, ZR2019ZD05, and ZR2020LZH013, and in part by EU H2020 RISE TESTBED2 Project under Grant 872172
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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