NDLI: Efficient QRM-MAP detector for spatial multiplexing MIMO systems

Content Provider	IEEE Xplore Digital Library
Author	Hyun-Myung Kim Dongsik Kim Tae-Kyoung Kim Gi-Hong Im
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Pohang Univ. of Sci. & Technol. (POSTECH), Pohang, South Korea (Hyun-Myung Kim; Dongsik Kim; Tae-Kyoung Kim; Gi-Hong Im)
Abstract	In this paper, we propose an efficient soft-detector for spatial multiplexing multiple-input multiple-output systems. Maximum a posteriori probability detection employing QR decomposition with M algorithm (QRM-MAP) achieves improved performance by adopting unbiased minimum-mean-square error (UMMSE) preprocessing. The efficiency of QRM-MAP can be further improved by using statistical information obtained from MMSE detection. Based on this information, the proposed detector orders tree branches and excludes insignificant branches in advance. To achieve low-and-fixed complexity, the node enumeration pattern that considers the greedy nature of the M algorithm is predetermined according to the node order. In addition, the soft information from MMSE can be used to compensate for the inherent performance loss caused by an insufficient number of candidates. Simulation results show that the proposed scheme provides an enhanced performance-complexity tradeoff as compared to the conventional schemes.
Starting Page	4003
Ending Page	4008
File Size	478065
Page Count	6
File Format	PDF
ISBN	9781467359382
ISSN	15253511
e-ISBN	9781467359399
e-ISBN	9781467359375
DOI	10.1109/WCNC.2013.6555217
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Complexity theory Detectors MIMO Measurement Vectors Matrix decomposition Reliability iterative detection and decoding (IDD) multiple-input multiple-output (MIMO) maximum-a-posteriori (MAP) detection
Content Type	Text
Resource Type	Article

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