NDLI: Improved QRM-ML detection with candidate selection for MIMO multiplexing systems

Content Provider	IEEE Xplore Digital Library
Author	Wei Peng Shaodan Ma Tung Sang Ng Jiang Zhou Wang
Copyright Year	2007
Description	Author affiliation: Univ. of Hong Kong, Hong Kong (Wei Peng; Shaodan Ma; Tung Sang Ng)
Abstract	This paper proposes an improved QR decomposition associated M algorithm for maximum-likelihood detection (QRM-ML) with candidate selection in multiple input multiple output (MIMO) multiplexing systems. In the proposed algorithm, only the points falling into a sub-set of constellation are selected as the candidates for each transmitted signal and included in the squared Euclidean distance calculation. Unlike the existing algorithms where the sub-set is fixed based on quadrant, the sub-set here is determined by the circle with center at the signal estimate and a pre-determined radius. By computer simulations, it is shown that the proposed QRM-ML algorithm can achieve the performance close to that of the existing QRM-ML algorithms with significantly reduced complexity.
Starting Page	1
Ending Page	4
File Size	146934
Page Count	4
File Format	PDF
ISBN	9781424412716
DOI	10.1109/TENCON.2007.4428838
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-30
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MIMO Computational complexity Computer simulation Iterative algorithms Bit error rate Receiving antennas Maximum likelihood detection Constellation diagram Euclidean distance Signal detection
Content Type	Text
Resource Type	Article

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