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Functional Properties of Minimum Mean-Square Error and Mutual Information
IEEE Transactions on Information Theory, 2012In addition to exploring its various regularity properties, we show that the minimum mean-square error (MMSE) is a concave functional of the input-output joint distribution. In the case of additive Gaussian noise, the MMSE is shown to be weakly continuous in the input distribution and Lipschitz continuous with respect to the quadratic Wasserstein ...
Yihong Wu, Sérgio Verdú
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Minimum Mean Square Error Vector Precoding
European Transactions on Telecommunications, 2005AbstractWe 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.
D. Schmidt, M. Joham, W. Utschick
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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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A minimum mean square error approach for speech enhancement
International Conference on Acoustics, Speech, and Signal Processing, 1990A minimum mean square error (MMSE) estimation approach for enhancing speech signals degraded by statistically independent additive noise is developed, based upon Gaussian autoregressive (AR) hidden Markov modeling of the clean signal and Gaussian AR modeling of the noise process.
Y. Ephraim
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Heat Flow Derivatives and Minimum Mean-Square Error in Gaussian Noise
IEEE Transactions on Information Theory, 2016Michel Ledoux
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Linear minimum mean square error estimation for discrete-time Markovian jump linear systems
IEEE Transactions on Automatic Control, 1994O L V Costa
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, 2021
In recent years, deep learning models have been employed for speech enhancement. Most of the existing methods based on deep learning use fully Convolutional Neural Network (CNN) to capture time–frequency information of input features. Compared with CNNs,
Zeyu Wang +3 more
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In recent years, deep learning models have been employed for speech enhancement. Most of the existing methods based on deep learning use fully Convolutional Neural Network (CNN) to capture time–frequency information of input features. Compared with CNNs,
Zeyu Wang +3 more
semanticscholar +1 more source
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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