A Low-complexity Near-ML Decoding Via Reduced Dimension Maximum Likelihood Search

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Titel: A Low-complexity Near-ML Decoding Via Reduced Dimension Maximum Likelihood Search
Autoren: Jun Won Choi, Byonghyo Shim, Andrew C. Singer
Weitere Verfasser: The Pennsylvania State University CiteSeerX Archives
Quelle: http://ipl.korea.ac.kr/paper/rd_lts.pdf.
Publikationsjahr: 2008
Bestand: CiteSeerX
Schlagwörter: Index Terms Maximum likelihood (ML) decoding, Sphere decoding, Minimum mean square error (MMSE, Multiple input multiple output (MIMO, Stack algorithm, Dimension reduction, List tree search
Beschreibung: In this paper, we consider a low-complexity ML detection technique referred to as reduced dimension ML search (RD-MLS). RD-MLS directly refers the division of symbols into two groups viz. strong and weak group for searching over vector space with strong symbols instead of whole symbols. The inevitable performance loss, due to the exclusion of weak symbols, is compensated by 1) the list tree search which is an extended version of single best searching algorithm called sphere decoding and 2) re-computation of weak symbols corresponding to each strong symbols found at the list tree search. Furthermore, in order to lessen the computational burden in list tree search, we employ tree pruning strategy that removes the unpromising subtrees before they are being searched. Two pruning techniques, called list sphere decoding with probabilistic pruning (LSD-PP) and list stack algorithm with probabilistic pruning (LSA-PP), is proposed for this purpose. From the simulations study on the multi-input-multi-output (MIMO) systems, we demonstrate that the RD-MLS shows near constant complexity over wide SNR range of interest (10−1 ∼ 10−4) while limiting the performance loss within a dB from the ML detection.
Publikationsart: text
Dateibeschreibung: application/pdf
Sprache: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.506.7780; http://ipl.korea.ac.kr/paper/rd_lts.pdf
Verfügbarkeit: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.506.7780
http://ipl.korea.ac.kr/paper/rd_lts.pdf
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