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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109079


    Title: An Eigen-based Approach for Enhancing Matrix Inversion Approximation in Massive MIMO Systems
    Authors: Kelvin Kuang-Chi Lee;Chiao-En Chen
    Keywords: Marcenko-Pastur law;Neumann series;matrix inversion;massive MIMO;zero-forcing;random matrix
    Date: 2016-10-26
    Issue Date: 2017-01-04 02:10:21 (UTC+8)
    Publisher: IEEE
    Abstract: This correspondence presents a new matrix inversion approximation (MIA) method for massive multiple-inputmultiple- output (MIMO) systems. In contrast to the existing methods which are mostly derived from the Neumann series expansion framework, additional coefficients have been introduced in our proposed method to enhance the precision of approximation. We propose an efficient algorithm for the coefficient design which consists of an eigenvalue estimation procedure derived from random matrix theory, and a least-squares fitting procedure that solves a low-dimension over-determined system of linear equations. Complexity analysis and simulation results show that our eigen-based MIA method exhibits practically comparable computational complexity while achieving substantial performance enhancement compared to other benchmark methods.
    Relation: IEEE Transactions on Vehicular Technology 66(6), p.5480-5484
    DOI: 10.1109/TVT.2016.2622010
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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