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    Analysis and Optimization of Massive MIMO Systems via Random Matrix Theory Approaches

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    Name:
    Final Ikram Boukhedimi Dissertation.pdf
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    2.486Mb
    Format:
    PDF
    Description:
    Final thesis
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    Type
    Dissertation
    Authors
    Boukhedimi, Ikram cc
    Advisors
    Alouini, Mohamed-Slim cc
    Committee members
    Laleg-Kirati, Taous-Meriem cc
    Al-Naffouri, Tareq Y. cc
    Kammoun, Abla cc
    Li, Yonghui
    Program
    Electrical and Computer Engineering
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2019-08-01
    Permanent link to this record
    http://hdl.handle.net/10754/656296
    
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    Abstract
    By endowing the base station with hundreds of antennas and relying on spatial multiplexing, massive multiple-input-multiple-output (MIMO) allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate and optimize the performance of massive MIMO in different contexts. First, we explore the spectral efficiency of massive MIMO in large-scale multi-tier heterogeneous networks that aim at network densification. This latter is epitomized by the joint implementation of massive MIMO and small cells to reap their benefits. Our interest is on the design of coordinated beamforming that mitigates cross-tier interference. Thus, we propose a regularized SLNR-based precoding in which the regularization factor is used to allow better resilience to channel estimation errors. Second, we move to studying massive MIMO under Line-of-Sight (LoS) propagation conditions. To this end, we carry out an analysis of the uplink (UL) of a massive MIMO system with per-user channel correlation and Rician factor. We start by analyzing conventional processing schemes such as LMMSE and MRC under training-based imperfect-channel-estimates, and then, propose a statistical combining technique that is more suitable in LoS-prevailing environments. Finally, we look into the interplay between LoS and the fundamental limitation of massive MIMO systems, namely, pilot contamination. We propose to analyze and compare the performance using single-cell and multi-cell detection methods. In this regard, the single-cell schemes are shown to produce higher SEs as the LoS strengthens, yet remain hindered by LoS-induced interference and pilot contamination. In contrast, for multi-cell combining, we analytically demonstrate that M-MMSE outperforms both single-cell detectors by generating a capacity that scales linearly with the number of antennas, and is further enhanced with LoS.
    Citation
    Boukhedimi, I. (2019). Analysis and Optimization of Massive MIMO Systems via Random Matrix Theory Approaches. KAUST Research Repository. https://doi.org/10.25781/KAUST-695S0
    DOI
    10.25781/KAUST-695S0
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-695S0
    Scopus Count
    Collections
    PhD Dissertations; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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