In this paper, we propose a new channel estimation scheme for downlink channels in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems where orthogonal frequency division multiplexing (OFDM) is adopted. In this scenario, the channel sparsity level is assumed unknown to the base station (BS). To the best of our knowledge, multitask bayesian compressive sensing (MBCS) has not been used in channel estimation of FDD massive MIMO systems. By exploiting the spatially common sparsity within the system bandwidth, the MBCS can learn the common sparsity characteristics of the user channels, which guarantees the performance of sparse channel recovery. Based on MBCS, we propose a pilot adapted MBCS (PAMBCS) scheme to further exploit the sparsity feature, where the pilot sequences are designed by minimizing the differential entropy of estimated channel vectors to reduce the estimation uncertainties. Simulation results have shown that the MBCS has a good capability to reduce pilot overhead, even though when a few number of subcarriers can be used for pilot transmission. Moreover, the performance of PAMBCS is much better than random pilots based MBCS.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Common Sparsity Based Channel Estimation for FDD Massive MIMO-OFDM Systems via Multitask Bayesian Compressive Sensing


    Beteiligte:
    Ji, Wei (Autor:in) / Qiu, Ling (Autor:in)


    Erscheinungsdatum :

    2017-09-01


    Format / Umfang :

    181778 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Channel Estimation for FDD Massive MIMO OFDM Systems

    Hu, Die / He, Lianghua | IEEE | 2017


    Bayesian Channel Estimation for Massive MIMO Communications

    Zhu, Chengzhi / Zheng, Zhitan / Jiang, Bin et al. | IEEE | 2016



    Channel Estimation for Massive MIMO-OFDM: Simplified Information Geometry Approach

    Yang, Jiyuan / Chen, Yan / Lu, An-An et al. | IEEE | 2023


    Bearing Estimation via Spatial Sparsity using Compressive Sensing

    Gurbuz, A. C. / Cevher, V. / Mcclellan, J. H. | IEEE | 2012