Sentence examples for use the linear minimum from inspiring English sources

In the study, we use the linear minimum mean squared error (LMMSE) estimators to estimate the source-to-relay link and the relay-to-destination link separately in an amplify-and-forward relaying system.

On the other hand, is the covariance of the estimation error of using the linear minimum mean squared error estimator instead.

By using the linear minimum mean square error (LMMSE) estimation algorithm, the destination first estimates the overall channels based on the pilot symbol inherent in frequency-shift keying (FSK) transmission.

Using the linear minimum mean square error (LMMSE) estimator [27] and assuming that all the channel responses are available at the FC, the estimation of s is (check {s} = mathbf {C}_{s mathbf {y}} mathbf {C}^{-1}_{mathbf {y} mathbf {y}}, mathbf {y}), where C s y is the covariance between s and y and C y y is the covariance of y.

In [1], by using the linear minimum-mean-square-error (LMMSE) combiner to optimally combine frequency offset estimators based on single time slot pilot samples from a received time-domain OFDM block, they successfully circumvent the multiple solution difficulty and meanwhile achieve very satisfactory joint estimation performance with wide tracking range (up to half the subcarrier spacing).

A new linear filter is designed in the linear minimum variance sense using the innovation analysis approach.

Local optimal linear estimators are designed by using the orthogonal projection principle, and the fusion estimators are designed by using a fusion rule weighted by matrices in the linear minimum variance sense.

Hence, it is possible to use either linear estimators, e.g., the Least Square (LS) or the Linear Minimum Mean Square Error estimator, to obtain the initial channel estimation.

In [15], Honig et al. propose a blind version of the linear minimum mean square error (LMMSE) detector, using constrained minimum output energy (CMOE) as the cost function for MAI suppression.

Based on the new augmented model, the local optimal linear filter is designed in the linear minimum variance sense.

AUC was computed using the linear (up)/logarithmic (down) trapezoidal rule with a minimum of four quantifiable concentrations.