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Minimum mean square error vector precoding
European Transactions on Telecommunications, 2006AbstractWe derive theminimum mean square error(MMSE) solution to vector precoding for frequency flat multiuser scenarios with a centralised multi‐antenna transmitter. The receivers employ a modulo operation, giving the transmitter the additional degree of freedom to choose aperturbation vector.
David A. Schmidt +2 more
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Truncated squarer with minimum mean-square error
Microelectronics Journal, 2014Abstract Squaring is an important arithmetic operation required in a multitude of applications. In this paper we present a truncated squarer that, with an n-bit input, produces its output on a number of bits that can be defined at design time in the [n,2n] range.
PETRA, NICOLA +4 more
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Consistency of the minimum mean square error estimate
ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005The minimum mean square error estimate for the deconvolution problem of a Gaussian signal in Gaussian noise is shown to be feasible in the sense of being inside closed convex sets defined by the noise statistics. It is pointed out that there is some a priori knowledge which is not satisfied by the Wiener solution but the set formed by this information ...
H. Joel Trussell, M. Reha Civanlar
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A Shrinkage Linear Minimum Mean Square Error Estimator
IEEE Signal Processing Letters, 2013The conventional linear minimum mean square error (LMMSE) estimator is commonly implemented through the sample covariance matrix. This estimator can only be implemented if the sample size N is higher than the observation dimension M. Moreover, this estimator performs poorly when the sample size is not sufficiently large.
Chao-Kai Wen +2 more
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Minimum mean-square error quadrature
Journal of Statistical Computation and Simulation, 1993Minimum mean squared error linear estimators of the area under a curve are considered for cases when the observations are observed with error. The underlying functional form giving rise to the observations is left unspecified, leading to use of quadrature estimators for the true area.
Walter W. Piegorsch, A. John Bailer
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Estimation of the minimum mean square error of prediction
Biometrika, 1975SUMMARY Bloomfield (1973) and Jones (1964) have discussed the estimation of the error of prediction of a time series. Their results use the asymptotic normality of their estimates and we attempt to examine the validity of this approximation in the simplest case.
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Minimum mean-squared error covariance shaping
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004The paper develops and explores applications of a linear shaping transformation that minimizes the mean squared error (MSE) between the original and shaped data, i.e., that results in an output vector with the desired covariance that is as close as possible to the input, in an MSE sense.
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On minimum mean square error speech enhancement
[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991Motivation for using minimum mean square error (MMSE) estimation in noisy speech enhancement problems is given. An MMSE estimator which is based on hidden Markov modeling of the clean signal as well as the noise process is systematically developed. The MMSE estimator is tested and compared with the spectral subtraction estimator in vector quantization ...
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