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- W2133231569 abstract "This paper proposes a likelihood estimation method for reduced-complexity maximum-likelihood (ML) detectors in a multiple-input multiple-output (MIMO) system. Reduced-complexity ML detectors, e.g., sphere decoder (SD) and QR decomposition (QRD)-M algorithm, are very promising candidates as a MIMO detector because they can estimate the ML or quasi-ML symbol with very low computational complexity. However, they may lose likelihood information about signal vectors having the opposite bit to hard decisions. Therefore, bit error rate performances of the reduced-complexity ML detectors are inferior to that of the ML detector when soft-decision decoding is employed. This paper proposes a simple estimation method of the lost likelihood information suitable for the reduced-complexity ML detectors. Computer simulation confirms that the proposed method provides excellent decoding performance, keeping the advantage of computational cost of the reduced-complexity ML detectors." @default.
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- W2133231569 date "2007-04-01" @default.
- W2133231569 modified "2023-09-25" @default.
- W2133231569 title "Likelihood Estimation for Reduced-Complexity ML Detectors in a MIMO System" @default.
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- W2133231569 doi "https://doi.org/10.1109/vetecs.2007.435" @default.
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