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    Low-Complexity Bayesian Estimation of Cluster-Sparse Channels

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    Type
    Article
    Authors
    Ballal, Tarig
    Al-Naffouri, Tareq Y. cc
    Ahmed, Syed
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2015-09-18
    Online Publication Date
    2015-09-18
    Print Publication Date
    2015-11
    Permanent link to this record
    http://hdl.handle.net/10754/578821
    
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    Abstract
    This paper addresses the problem of channel impulse response estimation for cluster-sparse channels under the Bayesian estimation framework. We develop a novel low-complexity minimum mean squared error (MMSE) estimator by exploiting the sparsity of the received signal profile and the structure of the measurement matrix. It is shown that due to the banded Toeplitz/circulant structure of the measurement matrix, a channel impulse response, such as underwater acoustic channel impulse responses, can be partitioned into a number of orthogonal or approximately orthogonal clusters. The orthogonal clusters, the sparsity of the channel impulse response and the structure of the measurement matrix, all combined, result in a computationally superior realization of the MMSE channel estimator. The MMSE estimator calculations boil down to simpler in-cluster calculations that can be reused in different clusters. The reduction in computational complexity allows for a more accurate implementation of the MMSE estimator. The proposed approach is tested using synthetic Gaussian channels, as well as simulated underwater acoustic channels. Symbol-error-rate performance and computation time confirm the superiority of the proposed method compared to selected benchmark methods in systems with preamble-based training signals transmitted over clustersparse channels.
    Citation
    Low-Complexity Bayesian Estimation of Cluster-Sparse Channels 2015:1 IEEE Transactions on Communications
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Communications
    DOI
    10.1109/TCOMM.2015.2480092
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7272056
    ae974a485f413a2113503eed53cd6c53
    10.1109/TCOMM.2015.2480092
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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