Efficient Sphere Detector Algorithm for Massive MIMO using GPU Hardware Accelerator

Handle URI:
http://hdl.handle.net/10754/613008
Title:
Efficient Sphere Detector Algorithm for Massive MIMO using GPU Hardware Accelerator
Authors:
Arfaoui, Mohamed-Amine; Ltaief, Hatem ( 0000-0002-6897-1095 ) ; Rezki, Zouheir; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 ) ; Keyes, David E. ( 0000-0002-4052-7224 )
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.
KAUST Department:
Extreme Computing Research Center; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Electrical Engineering Program; Applied Mathematics and Computational Science Program
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
Issue Date:
1-Jun-2016
DOI:
10.1016/j.procs.2016.05.377
Type:
Conference Paper
ISSN:
18770509
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S1877050916308523
Appears in Collections:
Conference Papers

Full metadata record

DC FieldValue Language
dc.contributor.authorArfaoui, Mohamed-Amineen
dc.contributor.authorLtaief, Hatemen
dc.contributor.authorRezki, Zouheiren
dc.contributor.authorAlouini, Mohamed-Slimen
dc.contributor.authorKeyes, David E.en
dc.date.accessioned2016-06-14T09:05:09Z-
dc.date.available2016-06-14T09:05:09Z-
dc.date.issued2016-06-01-
dc.identifier.citationEfficient Sphere Detector Algorithm for Massive MIMO using GPU Hardware Accelerator 2016, 80:2169 Procedia Computer Scienceen
dc.identifier.issn18770509-
dc.identifier.doi10.1016/j.procs.2016.05.377-
dc.identifier.urihttp://hdl.handle.net/10754/613008-
dc.description.abstractTo 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.en
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1877050916308523en
dc.rightsArchived with thanks to Procedia Computer Science, Under a Creative Commons license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectMassive MIMOen
dc.subjectSphere Decoder Algorithmen
dc.subjectGPU Accelerationen
dc.subjectReal-Time Systemsen
dc.titleEfficient Sphere Detector Algorithm for Massive MIMO using GPU Hardware Acceleratoren
dc.typeConference Paperen
dc.contributor.departmentExtreme Computing Research Centeren
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalProcedia Computer Scienceen
dc.conference.date6-8 June 2016en
dc.conference.nameInternational Conference on Computational Science 2016en
dc.conference.locationSan Diego, California, USAen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionTAMU, QAen
kaust.authorLtaief, Hatemen
kaust.authorRezki, Zouheiren
kaust.authorAlouini, Mohamed-Slimen
kaust.authorKeyes, David E.en
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