Low-Complexity Decoding via Reduced Dimension Maximum-Likelihood Search
- Authors
- Choi, Jun Won; Shim, Byonghyo; Singer, Andrew C.; Cho, Nam Ik
- Issue Date
- 3월-2010
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Dimension reduction; list tree search; maximum-likelihood (ML) decoding; minimum mean square error (MMSE); multiple input multiple output (MIMO); sphere decoding; stack algorithm
- Citation
- IEEE TRANSACTIONS ON SIGNAL PROCESSING, v.58, no.3, pp.1780 - 1793
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON SIGNAL PROCESSING
- Volume
- 58
- Number
- 3
- Start Page
- 1780
- End Page
- 1793
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/116847
- DOI
- 10.1109/TSP.2009.2036482
- ISSN
- 1053-587X
- Abstract
- In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable performance loss due to a reduction in the search space is compensated by 1) the use of a list tree search, which is an extension of a single best searching algorithm called sphere decoding, and 2) the recomputation of a set of weak symbols, i. e., those ignored in the reduced dimension search, for each strong symbol candidate found during the list tree search. Through simulations on M-quadrature amplitude modulation (QAM) transmission in frequency nonselective multi-input-multioutput (MIMO) channels, we demonstrate that the RD-MLS algorithm shows near constant complexity over a wide range of bit error rate (BER) (10(-1) similar to 10(-4)), while limiting performance loss to within 1 dB from ML detection.
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Collections - College of Informatics > Department of Computer Science and Engineering > 1. Journal Articles
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