García Mollá, VM.; Simarro Haro, MDLA.; Martínez Zaldívar, FJ.; González Salvador, A.; Vidal Maciá, AM. (2016). Maximum likelihood soft-output detection through Sphere Decoding combined with box optimization. Signal Processing. 125:249-260. doi:10.1016/j.sigpro.2016.02.006
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/82817
Title:
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Maximum likelihood soft-output detection through Sphere Decoding combined with box optimization
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Author:
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García Mollá, Víctor Manuel
Simarro Haro, Mª de los Angeles
Martínez Zaldívar, Francisco José
González Salvador, Alberto
Vidal Maciá, Antonio Manuel
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UPV Unit:
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Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació
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Issued date:
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Abstract:
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This paper focuses on the improvement of known algorithms for maximum likelihood
soft-output detection. These algorithms usually have large computational complexity, that
can be reduced by using clipping. Taking two ...[+]
This paper focuses on the improvement of known algorithms for maximum likelihood
soft-output detection. These algorithms usually have large computational complexity, that
can be reduced by using clipping. Taking two well-known soft-output maximum likelihood
algorithms (Repeated Tree Search and Single Tree Search) as a starting point, a
number of modifications (based mainly on box optimization techniques) are proposed to
improve the efficiency of the search. As a result, two new algorithms are proposed for
soft-output maximum likelihood detection. One of them is based on Repeated Tree Search
(which can be applied with and without clipping). The other one is based on Single Tree
Search, which can only be applied to the case with clipping. The proposed algorithms are
compared with the Single Tree Search algorithm, and their efficiency is evaluated in
standard detection problems (4 4 16-QAM and 4 4 64-QAM) with and without clipping.
The results show that the efficiency of the proposed algorithms is similar to that of
the Single Tree Search algorithm in the case 4 4 16-QAM; however, in the case 4 4 64-
QAM, the new algorithms are far more efficient than the Single Tree Search algorithm.
& 2016 Elsevier B.V. All rights reserved.
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Subjects:
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MIMO
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Soft-output maximum likelihood detection
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Copyrigths:
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Reserva de todos los derechos
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Source:
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Signal Processing. (issn:
0165-1684
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DOI:
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10.1016/j.sigpro.2016.02.006
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Publisher:
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Elsevier
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Publisher version:
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http://dx.doi.org/10.1016/j.sigpro.2016.02.006
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Project ID:
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Generalitat Valenciana/ ISIC/2012/006 and PROMETEO II/2014/003
Ministerio Espanol de Economia y Competitividad/ TEC2012-38142-C04 and RACHEL TEC2013-47141-C4-4-R
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Description:
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This is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing 125 (2016) 249–260. DOI 10.1016/j.sigpro.2016.02.006.
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Thanks:
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This work has been partially funded by Generalitat Valenciana through the projects ISIC/2012/006 and PROMETEO II/2014/003, and by Ministerio Espanol de Economia y Competitividad through the project TEC2012-38142-C04 and ...[+]
This work has been partially funded by Generalitat Valenciana through the projects ISIC/2012/006 and PROMETEO II/2014/003, and by Ministerio Espanol de Economia y Competitividad through the project TEC2012-38142-C04 and through the Grant RACHEL TEC2013-47141-C4-4-R.
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Type:
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Artículo
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