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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Zhang, Xing (author) / Chen, Fangjiong (author) / Feng, Jie (author) / Zhou, Maowu (author) / Yu, Hua (author)


    Publication date :

    2024-06-24


    Size :

    1565586 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Student-T Prior Sparse Bayesian Learning for Improved Channel Estimation in OTFS Systems

    Qiu, Wenduo / Zhang, Xiaoxu / Karagiannidis, George K. et al. | IEEE | 2024



    Low Complexity Detection Algorithms for OTFS under Rapidly Time-Varying Channel

    Li, Lingjun / Liang, Yu / Fan, Pingzhi et al. | IEEE | 2019