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    Efficient Sphere Detector Algorithm for Massive MIMO using GPU Hardware Accelerator

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    Description:
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    Type
    Conference Paper
    Authors
    Arfaoui, Mohamed-Amine
    Ltaief, Hatem cc
    Rezki, Zouheir cc
    Alouini, Mohamed-Slim cc
    Keyes, David E. cc
    KAUST Department
    Extreme Computing Research Center
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Applied Mathematics and Computational Science Program
    Date
    2016-06-02
    Online Publication Date
    2016-06-02
    Print Publication Date
    2016
    Permanent link to this record
    http://hdl.handle.net/10754/613008
    
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    Abstract
    To further enhance the capacity of next generation wireless communication systems, massive MIMO has recently appeared as a necessary enabling technology to achieve high performance signal processing for large-scale multiple antennas. However, massive MIMO systems inevitably generate signal processing overheads, which translate into ever-increasing rate of complexity, and therefore, such system may not maintain the inherent real-time requirement of wireless systems. We redesign the non-linear sphere decoder method to increase the performance of the system, cast most memory-bound computations into compute-bound operations to reduce the overall complexity, and maintain the real-time processing thanks to the GPU computational power. We show a comprehensive complexity and performance analysis on an unprecedented MIMO system scale, which can ease the design phase toward simulating future massive MIMO wireless systems.
    Citation
    Efficient Sphere Detector Algorithm for Massive MIMO using GPU Hardware Accelerator 2016, 80:2169 Procedia Computer Science
    Publisher
    Elsevier BV
    Journal
    Procedia Computer Science
    Conference/Event name
    International Conference on Computational Science 2016
    DOI
    10.1016/j.procs.2016.05.377
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S1877050916308523
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.procs.2016.05.377
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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