We propose a new Machine Learning (ML) approach to channel estimation (CE) in Massive Multiple Input Multiple Output (MIMO) receivers. The algorithm employs a recurrent neural network (RNN) for iterative channel tap search and nonlinear de-noising of their amplitudes in the time domain. Our method outperforms the sparse minimum mean square error (MMSE) CE that is based on time-domain windowing and a further linear denoising of channel taps within the window. Simulation results are presented for user speed of 5km/h in non-line-of-sight scenarios of the 5G QuaDRiGa 2.0 channel. The results are provided in both antenna and beamspace domains of the 64 antennas receiver.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Machine Learning-Assisted Channel Estimation in Massive MIMO Receiver


    Beteiligte:


    Erscheinungsdatum :

    01.04.2021


    Format / Umfang :

    2673441 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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

    Osinsky, Alexander / Ivanov, Andrey / Lakontsev, Dmitry et al. | IEEE | 2020


    LAS Receiver Exploiting Channel Hardening for Massive MIMO Systems

    Ammari, Mohamed Lassaad / Issa, Chihaoui / Chouinard, Jean-Yves | IEEE | 2019


    One-shot Learning for Channel Estimation in Massive MIMO Systems

    Kang, Kai / Hu, Qiyu / Cai, Yunlong et al. | IEEE | 2023


    Channel Estimation for FDD Massive MIMO OFDM Systems

    Hu, Die / He, Lianghua | IEEE | 2017