淡江大學機構典藏:Item 987654321/110489
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/110489


    Title: An Improved Matrix Inversion Approximation Method for Massive MIMO Systems with Transmit Antenna Correlation
    Authors: Kelvin Kuang-Chi Lee;Chiao-En Chen
    Keywords: Massive MIMO;antenna correlation;random matrix;free probability;Stieltjes inversion;S-transform
    Date: 2017-06-05
    Issue Date: 2017-07-05 02:10:55 (UTC+8)
    Abstract: Zero-forcing (ZF) precoding is known to achieve near-optimal performance at high signal-to-noise-ratio (SNR) in a massive MIMO system. While ZF precoding can be implemented by exact matrix inversion, this scheme is often prohibited due to stability and complexity issues. Hence a few matrix inverse approximation (MIA) methods were introduced to address these concerns. An eigen-based algorithm was recently proposed to enhance MIA accuracy for uncorrelated Rayleigh fading channel by estimating asymptotic eigenvalues of a Wishart matrix following the random matrix theory. In this paper, the eigen-based MIA concept is extended to the one-sided Kronecker model, (i.e., Rayleigh fading channel with transmit antenna correlation). Asymptotic eigenvalue probability density function of the non-commutative Kronecker channel is constructed from free probability theory and applied in eigen-based MIA. Simulation shows that MIA is degraded by transmit antenna correlation. However, compared to other Neumann expansion based methods, the proposed eigen-based MIA precoding scheme is still less susceptible to transmit antenna correlation especially for low to mid correlation values.
    Relation: IEEE 2017 SPAWC ( International Workshop on Signal Processing advances in Wireless Communications)
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Proceeding

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    An Improved Matrix Inversion Approximation Method for Massive MIMO Systems with Transmit Antenna Correlation.pdf350KbAdobe PDF2View/Open
    IEEE 2017 SPAWC _議程.pdf4004KbAdobe PDF299View/Open

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