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- W2122161872 abstract "In this paper, we reveal that a maximum-likelihood detection (MLD) for multiple-input multiple-output (MIMO) systems requires not exponential complexity order in multiplication operations but cubic order against the number of antennas, by introducing the so-called correlation metric. However, the MIMO-MLD requires still exponential order in arithmetic additions. In order to further reduce the complexity, we propose effective techniques referred to as a metric-segmentation and a norm-constraint approach. We show that the proposed MLD using these techniques can significantly reduce the computational complexity without any performance degradation. Moreover, the proposal does not need additional high-complexity operations such as QR decompositions and matrix inversions." @default.
- W2122161872 created "2016-06-24" @default.
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- W2122161872 date "2005-12-10" @default.
- W2122161872 modified "2023-09-26" @default.
- W2122161872 title "Metric-segmented Low-complexity ML Detection for Spectrum-efficient Multiple-antenna Systems" @default.
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- W2122161872 doi "https://doi.org/10.1109/vetecs.2005.1543599" @default.
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