This paper proposes a sparse Bayesian learning (SBL)-based channel estimation algorithm for orthogonal time frequency space (OTFS) systems. By introducing the basis expansion model (BEM), we reconstruct the original high-dimensional time-varying channel matrix to a low-dimensional weighted combination of basis functions. Then, we propose a two-stage SBL model that estimates the coefficients of the basis functions by leveraging the sparseness of the channel in the delay-Doppler (DD) domain. Further, an iterative decision feedback scheme is proposed to refine the accuracy of channel estimation and symbol detection. Simulation results show that even with a few iterations, the accuracy of channel estimation can be significantly improved. Besides, the proposed data-aided two-stage SBL-based OTFS channel estimation algorithm outperforms the existing OTFS receivers exactly in terms of estimation mean square error and bit error rate.


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    Titel :

    BEM Based Channel Estimation via Sparse Bayesian Learning for OTFS over Fast Time-Varying Channel


    Beteiligte:
    Zhang, Xing (Autor:in) / Chen, Fangjiong (Autor:in) / Feng, Jie (Autor:in) / Zhou, Maowu (Autor:in) / Yu, Hua (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    1565586 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch