Evaluation framework for K-best sphere decoders

Handle URI:
http://hdl.handle.net/10754/561492
Title:
Evaluation framework for K-best sphere decoders
Authors:
Shen, Chungan; Eltawil, Ahmed M.; Salama, Khaled N. ( 0000-0001-7742-1282 )
Abstract:
While Maximum-Likelihood (ML) is the optimum decoding scheme for most communication scenarios, practical implementation difficulties limit its use, especially for Multiple Input Multiple Output (MIMO) systems with a large number of transmit or receive antennas. Tree-searching type decoder structures such as Sphere decoder and K-best decoder present an interesting trade-off between complexity and performance. Many algorithmic developments and VLSI implementations have been reported in literature with widely varying performance to area and power metrics. In this semi-tutorial paper we present a holistic view of different Sphere decoding techniques and K-best decoding techniques, identifying the key algorithmic and implementation trade-offs. We establish a consistent benchmark framework to investigate and compare the delay cost, power cost, and power-delay-product cost incurred by each method. Finally, using the framework, we propose and analyze a novel architecture and compare that to other published approaches. Our goal is to explicitly elucidate the overall advantages and disadvantages of each proposed algorithms in one coherent framework. © 2010 World Scientific Publishing Company.
KAUST Department:
Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Sensors Lab
Publisher:
World Scientific Pub Co Pte Lt
Journal:
Journal of Circuits, Systems and Computers
Issue Date:
Aug-2010
DOI:
10.1142/S0218126610006554
Type:
Article
ISSN:
02181266
Appears in Collections:
Articles; Electrical Engineering Program; Sensors Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorShen, Chunganen
dc.contributor.authorEltawil, Ahmed M.en
dc.contributor.authorSalama, Khaled N.en
dc.date.accessioned2015-08-02T09:12:41Zen
dc.date.available2015-08-02T09:12:41Zen
dc.date.issued2010-08en
dc.identifier.issn02181266en
dc.identifier.doi10.1142/S0218126610006554en
dc.identifier.urihttp://hdl.handle.net/10754/561492en
dc.description.abstractWhile Maximum-Likelihood (ML) is the optimum decoding scheme for most communication scenarios, practical implementation difficulties limit its use, especially for Multiple Input Multiple Output (MIMO) systems with a large number of transmit or receive antennas. Tree-searching type decoder structures such as Sphere decoder and K-best decoder present an interesting trade-off between complexity and performance. Many algorithmic developments and VLSI implementations have been reported in literature with widely varying performance to area and power metrics. In this semi-tutorial paper we present a holistic view of different Sphere decoding techniques and K-best decoding techniques, identifying the key algorithmic and implementation trade-offs. We establish a consistent benchmark framework to investigate and compare the delay cost, power cost, and power-delay-product cost incurred by each method. Finally, using the framework, we propose and analyze a novel architecture and compare that to other published approaches. Our goal is to explicitly elucidate the overall advantages and disadvantages of each proposed algorithms in one coherent framework. © 2010 World Scientific Publishing Company.en
dc.publisherWorld Scientific Pub Co Pte Lten
dc.subjectDecodingen
dc.subjectK-besten
dc.subjectMIMOen
dc.subjecttree searchen
dc.titleEvaluation framework for K-best sphere decodersen
dc.typeArticleen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentSensors Laben
dc.identifier.journalJournal of Circuits, Systems and Computersen
dc.contributor.institutionDepartment of Electrical Engineering and Computer Science, University of California, Irvine, CA, United Statesen
kaust.authorSalama, Khaled N.en
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