Data-Aided LS Channel Estimation in Massive MIMO Turbo-Receiver

Alexander Osinsky, Andrey Ivanov, Dmitriy Lakontsev, Roman Bychkov, Dmitry Yarotskiy

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

    5 Citations (Scopus)


    In this paper, we propose a new algorithm of iterative least squared (LS) channel estimation for 64 antennas Massive Multiple Input, Multiple Output (MIMO) turbo-receiver. The algorithm employs log-likelihood ratios (LLR) of low-density parity-check (LDPC) decoder and minimum mean square error (MMSE) estimator to achieve soft data symbols. These soft data symbols are further MMSE-weighted again and combined with pilot symbols to achieve a modified LS channel estimate. The modified LS estimate is employed by the same channel estimation unit to enhance turbo-receiver performance via channel re-estimation, as a result, the proposed approach has low complexity and fits any channel estimation solution, which is quite valuable in practice. We analyze both hard and soft algorithm versions and present simulation results of 5G turbo-receiver in the 3D-UMa model of the QuaDRiGa 2.0 channel. Simulation results demonstrate up to 0. 3dB performance gain compared to the unweighted hard data symbols utilization in the LS channel re-calculation.

    Original languageEnglish
    Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728152073
    Publication statusPublished - May 2020
    Event91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
    Duration: 25 May 202028 May 2020

    Publication series

    NameIEEE Vehicular Technology Conference
    ISSN (Print)1550-2252


    Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020


    • Channel estimation
    • Massive MIMO
    • Turbo-receiver


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