A Computationally Efficient Signal Detection Method for Spatially Multiplexed MIMO Systems 


Vol. 32,  No. 7, pp. 616-626, Jul.  2007


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  Abstract

In spatially multiplexed MIMO systems that enable high data rate transmission over wireless communication channels, the spatial demultiplexing at the receiver is a challenging task, and various demultiplexing methods have been developed recently by many researchers. Among the previous methods, maximum likelihood detection with QR decomposition and M-algorithm (QRM-MLD), and sphere decoding (SD) schemes have been reported to achieve a (near) maximum likelihood (ML) performance. In this paper, we propose a novel signal detection method that achieves a near ML performance in a computationally efficient manner. The proposed method is demonstrated via a set of computer simulations that the proposed method achieves a near ML performance while requiring a complexity that is comparable to that of the conventional MMSE-OSIC. We also show that the loglikelihood ratio (LLR) values for all bits are obtained without additional calculation but as byproduct in the proposed detection method, while in the previous QRM-MLD, SD, additional computation is necessary after the hard decision for LLR calculation.

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  Cite this article

[IEEE Style]

T. Im, J. Kim, J. Yi, S. Yun, Y. S. Cho, "A Computationally Efficient Signal Detection Method for Spatially Multiplexed MIMO Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 32, no. 7, pp. 616-626, 2007. DOI: .

[ACM Style]

Tae-Ho Im, Jae-Kwon Kim, Joo-Hyun Yi, Sang-boh Yun, and Yong Soo Cho. 2007. A Computationally Efficient Signal Detection Method for Spatially Multiplexed MIMO Systems. The Journal of Korean Institute of Communications and Information Sciences, 32, 7, (2007), 616-626. DOI: .

[KICS Style]

Tae-Ho Im, Jae-Kwon Kim, Joo-Hyun Yi, Sang-boh Yun, Yong Soo Cho, "A Computationally Efficient Signal Detection Method for Spatially Multiplexed MIMO Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 32, no. 7, pp. 616-626, 7. 2007.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880